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data/ds_config/ds_config_stage1.json

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+{
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+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 12,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": "auto",
+            "betas": "auto",
+            "eps": "auto",
+            "weight_decay": "auto"
+        }
+    },
+
+    "scheduler": {
+        "type": "WarmupDecayLR",
+        "params": {
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+            "warmup_max_lr": "auto",
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+            "total_num_steps": "auto"
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+    "steps_per_print": 30,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}

data/ds_config/ds_config_stage2.json

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+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
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+    "bf16": {
+        "enabled": "auto"
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+    "zero_optimization": {
+        "stage": 2,
+        "allgather_partitions": true,
+        "allgather_bucket_size": 5e8,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "reduce_bucket_size": 5e8,
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+
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "steps_per_print": 2000,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}

data/ds_config/ds_config_stage3.json

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+{
+  "bf16": {
+    "enabled": "auto"
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
+  "scheduler": {
+    "type": "WarmupDecayLR",
+    "params": {
+      "total_num_steps": "auto",
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto"
+    }
+  },
+  "zero_optimization": {
+    "stage": 3,
+
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "sub_group_size": 1e9,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 1e9,
+    "stage3_max_reuse_distance": 1e9,
+    "stage3_gather_16bit_weights_on_model_save": true
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "steps_per_print": 10,
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

data/outputs/90k_0104+8-longalpaca_0106/args/data_args.json

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+{
+    "dataset_list": [
+        "longalpaca_0106"
+    ],
+    "dataset_save_dir": "/home/baaiks/ninglu/data/PluginTransformer/"
+}

data/outputs/90k_0104+8-longalpaca_0106/args/model_args.json

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+{
+    "model_name_or_path": "meta-llama/Llama-2-7b-chat-hf",
+    "super_tokenizer_name_or_path": "/home/baaiks/ninglu/code/PluginTransformer/data/outputs/90k_0104+8/checkpoint-30000/super_tokenizer",
+    "cache_dir": "/home/baaiks/shared_models",
+    "super_tokenizer_num_hidden_layers": 8,
+    "is_model_frozen": true,
+    "use_flash_attention_2": true,
+    "dtype": "bf16",
+    "device_map": null
+}

data/outputs/90k_0104+8-longalpaca_0106/args/training_args.json

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+{
+    "output_dir": "data/outputs/90k_0104+8-longalpaca_0106",
+    "overwrite_output_dir": false,
+    "do_train": false,
+    "do_eval": false,
+    "do_predict": false,
+    "evaluation_strategy": "no",
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+    "warmup_steps": 0,
+    "log_level": "passive",
+    "log_level_replica": "warning",
+    "log_on_each_node": true,
+    "logging_dir": "data/outputs/90k_0104+8-longalpaca_0106/runs/Jan07_00-37-19_cbc51e0ccea4",
+    "logging_strategy": "steps",
+    "logging_first_step": false,
+    "logging_steps": 50,
+    "logging_nan_inf_filter": true,
+    "save_strategy": "steps",
+    "save_steps": 850,
+    "save_total_limit": null,
+    "save_safetensors": false,
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+    "seed": 42,
+    "data_seed": null,
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+    "run_name": "data/outputs/90k_0104+8-longalpaca_0106",
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+    "deepspeed": "data/ds_config/ds_config_stage1.json",
+    "label_smoothing_factor": 0.0,
+    "optim": "adamw_torch",
+    "optim_args": null,
+    "adafactor": false,
+    "group_by_length": false,
+    "length_column_name": "length",
+    "report_to": [],
+    "ddp_find_unused_parameters": null,
+    "ddp_bucket_cap_mb": null,
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+    "dataloader_pin_memory": true,
+    "skip_memory_metrics": true,
+    "use_legacy_prediction_loop": false,
+    "push_to_hub": false,
+    "resume_from_checkpoint": null,
+    "hub_model_id": null,
+    "hub_strategy": "every_save",
+    "hub_token": "<HUB_TOKEN>",
+    "hub_private_repo": false,
+    "hub_always_push": false,
+    "gradient_checkpointing": true,
+    "include_inputs_for_metrics": false,
+    "fp16_backend": "auto",
+    "push_to_hub_model_id": null,
+    "push_to_hub_organization": null,
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+}

data/outputs/90k_0104+8-longalpaca_0106/command.log

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+Time      : "2024-01-07, 00:38:42"
+Command   : "/home/baaiks/ninglu/code/PluginTransformer/main/train.py --local_rank=0 --model_name_or_path meta-llama/Llama-2-7b-chat-hf --super_tokenizer_name_or_path /home/baaiks/ninglu/code/PluginTransformer/data/outputs/90k_0104+8/checkpoint-30000/super_tokenizer --super_tokenizer_num_hidden_layers 8 --dataset_list longalpaca_0106 --output_dir data/outputs/90k_0104+8-longalpaca_0106 --learning_rate 1e-5 --num_train_epochs 1 --per_device_train_batch_size 1 --max_step 850 --logging_strategy steps --logging_steps 50 --save_strategy steps --save_steps 850 --gradient_checkpointing --deepspeed data/ds_config/ds_config_stage1.json"

data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer/config.json

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+{
+  "_name_or_path": "/home/baaiks/ninglu/code/PluginTransformer/data/outputs/90k_0104+8/checkpoint-30000/super_tokenizer",
+  "architectures": [
+    "SuperTokenizer"
+  ],
+  "attention_bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 8,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer/pytorch_model.bin

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data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer/tokenizer.model

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data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "bos_token": "<s>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": true
+}

data/outputs/90k_0104+8-longalpaca_0106/train.log

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+[2024-01-07 00:37:12,086] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-01-07 00:37:13,422] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
+Detected CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7: setting --include=localhost:0,1,2,3,4,5,6,7
+[2024-01-07 00:37:13,422] [INFO] [runner.py:571:main] cmd = /home/baaiks/ninglu/envs/snl/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=12345 --module --enable_each_rank_log=None main.train --model_name_or_path meta-llama/Llama-2-7b-chat-hf --super_tokenizer_name_or_path /home/baaiks/ninglu/code/PluginTransformer/data/outputs/90k_0104+8/checkpoint-30000/super_tokenizer --super_tokenizer_num_hidden_layers 8 --dataset_list longalpaca_0106 --output_dir data/outputs/90k_0104+8-longalpaca_0106 --learning_rate 1e-5 --num_train_epochs 1 --per_device_train_batch_size 1 --max_step 850 --logging_strategy steps --logging_steps 50 --save_strategy steps --save_steps 850 --gradient_checkpointing --deepspeed data/ds_config/ds_config_stage1.json
+[2024-01-07 00:37:15,309] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-01-07 00:37:16,267] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}
+[2024-01-07 00:37:16,267] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0
+[2024-01-07 00:37:16,267] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]})
+[2024-01-07 00:37:16,267] [INFO] [launch.py:163:main] dist_world_size=8
+[2024-01-07 00:37:16,267] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+[2024-01-07 00:37:19,714] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
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+[2024-01-07 00:37:19,891] [INFO] [comm.py:637:init_distributed] cdb=None
+[2024-01-07 00:37:19,892] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-01-07 00:37:19,913] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-01-07 00:37:19,913] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
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data/outputs/90k_0104+8/training_args.bin

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+oid sha256:bace9046f9d4dab42afd685226ead9374cfd8f0c5b0df8707cd387742d01d8ba
+size 6008

data/results/longbench/test/args/data_args.json

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+{
+    "dataset_list": [
+        "gov_report",
+        "qmsum",
+        "multi_news"
+    ],
+    "dataset_save_dir": "/share/ninglu_shao/data/PluginTransformer"
+}

data/results/longbench/test/args/longbench_args.json

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+{
+    "cpu": false,
+    "seed": 2023,
+    "output_dir": "data/results/longbench/test",
+    "max_length": 3500,
+    "super_tokenizer_max_length": 4096,
+    "batch_size": 2,
+    "compression_ratio": 32,
+    "down_scale_method": "uniform"
+}

data/results/longbench/test/args/model_args.json

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+{
+    "model_name_or_path": "lmsys/vicuna-7b-v1.5-16k",
+    "super_tokenizer_name_or_path": "/share/ninglu_shao/code/PluginTransformer/data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer",
+    "cache_dir": "/share/LMs",
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+}

data/results/longbench/test/command.log

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+Time      : "2024-03-29, 19:57:27"
+Command   : "/share/ninglu_shao/code/PluginTransformer/main/eval_longbench.py --model_name_or_path lmsys/vicuna-7b-v1.5-16k --super_tokenizer_name_or_path /share/ninglu_shao/code/PluginTransformer/data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer --super_tokenizer_num_hidden_layers 8 --dataset_list gov_report,qmsum,multi_news --output_dir data/results/longbench/test --max_length 3500 --batch_size 2 --compression_ratio 32 --down_scale_method uniform"

data/results/longbench/test/eval_longbench.log

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+***** gov_report metrics *****
+  avg_token_num =            3499.955
+  rouge_score   = 0.31805211042271564
+***** qmsum metrics *****
+  avg_token_num =             3499.52
+  rouge_score   = 0.20486288774116612
+***** multi_news metrics *****
+  avg_token_num =            3193.62
+  rouge_score   = 0.2786549030620904

data/results/longbench/test/generation/2wikimqa.json

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+[
+    {
+        "output": "Transylvania.",
+        "answer": [
+            "Ozalj"
+        ]
+    },
+    {
+        "output": "The given passage does not mention Sobe or Saint Anne.",
+        "answer": [
+            "John the Baptist"
+        ]
+    },
+    {
+        "output": "The director of film Man At Bath is not mentioned in the given passages.",
+        "answer": [
+            "Cahiers du cinéma"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "The date of birth of William Paulet, 3rd Marquess of Winchester's father is not given in the passages.",
+        "answer": [
+            "1510"
+        ]
+    },
+    {
+        "output": "The given passages do not mention Edward Watson, Viscount Sondes's paternal grandfather.",
+        "answer": [
+            "Edward Watson"
+        ]
+    },
+    {
+        "output": "The date of death of Humphrey De Bohun, 7th Earl of Hereford's father is not mentioned in the given passages.",
+        "answer": [
+            "16 September 1360"
+        ]
+    },
+    {
+        "output": "Helmichis's father-in-law is not mentioned in the given passages.",
+        "answer": [
+            "Cunimund"
+        ]
+    },
+    {
+        "output": "Paul Vincent Carroll studied at St Patrick's College in Dublin.",
+        "answer": [
+            "St Patrick's College"
+        ]
+    },
+    {
+        "output": "Katherine Stenholm worked at Bob Jones University as the founding director of Unusual Films.",
+        "answer": [
+            "Bob Jones University"
+        ]
+    },
+    {
+        "output": "The Secret Invasion.",
+        "answer": [
+            "The House Of The Seven Hawks"
+        ]
+    },
+    {
+        "output": "Marie of Brabant, Queen of France's paternal grandmother is Margaret of Brabant.",
+        "answer": [
+            "Marie of Hohenstaufen"
+        ]
+    },
+    {
+        "output": "The question does not provide enough information to determine who Renaud II, Count of Soissons's uncle is.",
+        "answer": [
+            "Guy II, Count of Soissons"
+        ]
+    },
+    {
+        "output": "The given passages do not provide a clear answer to this question.",
+        "answer": [
+            "26 April 1872"
+        ]
+    },
+    {
+        "output": "The director of film Renegade Force is from the United States.",
+        "answer": [
+            "America"
+        ]
+    },
+    {
+        "output": "The spouse of the director of film Emergency Wedding is Gertrude Stein.",
+        "answer": [
+            "Ona Munson"
+        ]
+    },
+    {
+        "output": "Indradhanura Chhai.",
+        "answer": [
+            "The Death Of Black King"
+        ]
+    },
+    {
+        "output": "Yes, both films have the same director, Clara Law.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "India",
+        "answer": [
+            "Mysore"
+        ]
+    },
+    {
+        "output": "Passage 4 mentions Beaulieu-sur-Loire, which is the place of death of Jacques MacDonald, a French general who served in",
+        "answer": [
+            "Kristiania"
+        ]
+    },
+    {
+        "output": "The performer of the song I'll Say It is Adam Schlesinger. He graduated from the Berklee College of Music.",
+        "answer": [
+            "Lee Strasberg Theatre and Film Institute"
+        ]
+    },
+    {
+        "output": "Hell Up In Harlem.",
+        "answer": [
+            "Hell Up In Harlem"
+        ]
+    },
+    {
+        "output": "Cipriano Castro.",
+        "answer": [
+            "Cipriano Castro"
+        ]
+    },
+    {
+        "output": "The Year Of The Rabbit.",
+        "answer": [
+            "Monster On The Campus"
+        ]
+    },
+    {
+        "output": "The information in the given passages does not provide enough context to determine who is younger between Guy Arvely Dolsin and Altuğ Çel",
+        "answer": [
+            "Altuğ Çelikbilek"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "Jugband Blues"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "The given passages do not mention Baldwin I Rátót.",
+        "answer": [
+            "Leustach Rátót"
+        ]
+    },
+    {
+        "output": "Bhaktapur",
+        "answer": [
+            "Nepal"
+        ]
+    },
+    {
+        "output": "Archibald Acheson, 4th Earl of Gosford's paternal grandfather is not mentioned in the given passages.",
+        "answer": [
+            "Archibald Acheson, 2nd Earl of Gosford"
+        ]
+    },
+    {
+        "output": "Zhao Shoushan.",
+        "answer": [
+            "Jean Paul Getty"
+        ]
+    },
+    {
+        "output": "The performer of song Fantasy (George Michael Song) died in Oxford, England.",
+        "answer": [
+            "Goring-on-Thames, Oxfordshire"
+        ]
+    },
+    {
+        "output": "Yes, they are both British.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Wooden Crosses.",
+        "answer": [
+            "Wooden Crosses"
+        ]
+    },
+    {
+        "output": "Above Rubies.",
+        "answer": [
+            "Above Rubies"
+        ]
+    },
+    {
+        "output": "Blue Blood And Red.",
+        "answer": [
+            "Blue Blood And Red"
+        ]
+    },
+    {
+        "output": "The place of birth for the director of film Clowning Around is not mentioned in the given passages.",
+        "answer": [
+            "Castlemaine, Victoria, Australia"
+        ]
+    },
+    {
+        "output": "Paul De Scherff.",
+        "answer": [
+            "Lyudmyla Olyanovska"
+        ]
+    },
+    {
+        "output": "The passage does not mention the place of death of Kamures Kadın's husband.",
+        "answer": [
+            "Constantinople"
+        ]
+    },
+    {
+        "output": "Passage 3",
+        "answer": [
+            "East Francia"
+        ]
+    },
+    {
+        "output": "Edwin L. Marin, the director of Henry Goes Arizona, died first.",
+        "answer": [
+            "Henry Goes Arizona"
+        ]
+    },
+    {
+        "output": "The performer of the song \"I Believe In Your Sweet Love\" is Bonnie Tyler.",
+        "answer": [
+            "United Kingdom"
+        ]
+    },
+    {
+        "output": "All-American Co-Ed.",
+        "answer": [
+            "All-American Co-Ed"
+        ]
+    },
+    {
+        "output": "Buenos Aires",
+        "answer": [
+            "Buenos Aires"
+        ]
+    },
+    {
+        "output": "The mother-in-law of Louise Of Mecklenburg-Güstrow is Anna Sophie Reventlow.",
+        "answer": [
+            "Charlotte Amalie of Hesse-Kassel"
+        ]
+    },
+    {
+        "output": "The place of death of Agnes Of Hohenstaufen's husband is not mentioned in the given passages.",
+        "answer": [
+            "Brunswick"
+        ]
+    },
+    {
+        "output": "Marcus Annius Libo's aunt is Annia Fundania Faustina.",
+        "answer": [
+            "Vibia Sabina"
+        ]
+    },
+    {
+        "output": "Bajo Otro Sol was released more recently than Riding The California Trail.",
+        "answer": [
+            "Bajo Otro Sol"
+        ]
+    },
+    {
+        "output": "Sue Nelson",
+        "answer": [
+            "Marie Laforêt"
+        ]
+    },
+    {
+        "output": "The spouse of the director of film Streets Of Blood is Maria Teresa, Grand Duchess of Luxembourg.",
+        "answer": [
+            "Sandra Nelson"
+        ]
+    },
+    {
+        "output": "Henrietta Maria Of Brandenburg-Schwedt's father's birthday is not mentioned in the given passages.",
+        "answer": [
+            "May 19, 1669"
+        ]
+    },
+    {
+        "output": "Sancha of Castile, Queen of Navarre's mother died in the city of León on 8 November 1067. She was inter",
+        "answer": [
+            "Palencia"
+        ]
+    },
+    {
+        "output": "Dance With A Stranger.",
+        "answer": [
+            "Miley Naa Miley Hum"
+        ]
+    },
+    {
+        "output": "Space Probe Taurus.",
+        "answer": [
+            "Tom Mix In Arabia"
+        ]
+    },
+    {
+        "output": "Sergio Bergonzelli died later than Jean Rollin.",
+        "answer": [
+            "Seven In The Sun"
+        ]
+    },
+    {
+        "output": "Dr. Socrates.",
+        "answer": [
+            "Dr. Socrates"
+        ]
+    },
+    {
+        "output": "Woman In The Moon.",
+        "answer": [
+            "Changeland"
+        ]
+    },
+    {
+        "output": "Peter Rosegger.",
+        "answer": [
+            "Ruel Redinger"
+        ]
+    },
+    {
+        "output": "Pyotr Karatygin's sibling-in-law is Robert Vadra.",
+        "answer": [
+            "Alexandra Kolosova"
+        ]
+    },
+    {
+        "output": "Jessi Colter",
+        "answer": [
+            "Jessi Colter"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Erich Haenisch.",
+        "answer": [
+            "William Pooley"
+        ]
+    },
+    {
+        "output": "The performer of the song You Can is Bernie Bonvoisin.",
+        "answer": [
+            "Sweden"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Melody Of The World.",
+        "answer": [
+            "Melody Of The World"
+        ]
+    },
+    {
+        "output": "The place of death of the composer of film Miracle Of Marcelino is not mentioned in the given passages.",
+        "answer": [
+            "Madrid"
+        ]
+    },
+    {
+        "output": "The mother of the director of film Atomised (Film) is not mentioned in the given passages.",
+        "answer": [
+            "Gisela Elsner"
+        ]
+    },
+    {
+        "output": "The Great Man's Lady.",
+        "answer": [
+            "La Belle Américaine"
+        ]
+    },
+    {
+        "output": "The given passages do not mention the place of birth of the director of film Sweepstakes.",
+        "answer": [
+            "Oklahoma City, Oklahoma"
+        ]
+    },
+    {
+        "output": "Dubai.",
+        "answer": [
+            "Dubai"
+        ]
+    },
+    {
+        "output": "The performer of song Égérie was born in France.",
+        "answer": [
+            "La Trinité"
+        ]
+    },
+    {
+        "output": "Ludwig von Westphalen",
+        "answer": [
+            "Prince Albert of Prussia"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Beaulieu-sur-Loire.",
+        "answer": [
+            "Banbury"
+        ]
+    },
+    {
+        "output": "Sir William Gore, 3rd Baronet's paternal grandfather is Sir Ralph Gore, 2nd Baronet.",
+        "answer": [
+            "Sir Paul Gore, 1st Baronet"
+        ]
+    },
+    {
+        "output": "Nizar Al-Mustafa's father is from Egypt.",
+        "answer": [
+            "Fatimid caliphate"
+        ]
+    },
+    {
+        "output": "The given passages do not mention the birthplace of Lou Breslow's wife.",
+        "answer": [
+            "Dayton, Ohio"
+        ]
+    },
+    {
+        "output": "The spouse of the director of film Ann Carver's Profession is not mentioned in the given passages.",
+        "answer": [
+            "Ona Munson"
+        ]
+    },
+    {
+        "output": "The Drover's Sweetheart.",
+        "answer": [
+            "The Drover'S Sweetheart"
+        ]
+    },
+    {
+        "output": "The place of birth of the director of film Bhagirathi is not mentioned in the given passages.",
+        "answer": [
+            "Tumkur"
+        ]
+    },
+    {
+        "output": "Closely Watched Trains.",
+        "answer": [
+            "Det Sande Ansigt"
+        ]
+    },
+    {
+        "output": "Il Gaucho.",
+        "answer": [
+            "Bomgay"
+        ]
+    },
+    {
+        "output": "The given passages do not mention the place of death of Maria's husband.",
+        "answer": [
+            "Preslav"
+        ]
+    },
+    {
+        "output": "The PyraMMMid.",
+        "answer": [
+            "Revolt Of The Praetorians"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "The cause of death of Constantia Eriksdotter's father is not mentioned in the given passages.",
+        "answer": [
+            "poisoning"
+        ]
+    },
+    {
+        "output": "American",
+        "answer": [
+            "America"
+        ]
+    },
+    {
+        "output": "Hawaii",
+        "answer": [
+            "Kingdom of Hawaii"
+        ]
+    },
+    {
+        "output": "X-Paroni.",
+        "answer": [
+            "X-Paroni"
+        ]
+    },
+    {
+        "output": "Vasantha Raagam was released earlier.",
+        "answer": [
+            "Vasantha Raagam"
+        ]
+    },
+    {
+        "output": "The given passages do not mention a person named Hong Ra-Hee.",
+        "answer": [
+            "Lee Byung-chul"
+        ]
+    },
+    {
+        "output": "Charles of Valois.",
+        "answer": [
+            "John I, Duke of Bourbon"
+        ]
+    },
+    {
+        "output": "The date of death of Duleep Singh's father is not given in the given passages.",
+        "answer": [
+            "27 June 1839"
+        ]
+    },
+    {
+        "output": "The given passage does not mention the place of birth of the director of film Gunsmoke.",
+        "answer": [
+            "Gura Humorului"
+        ]
+    },
+    {
+        "output": "The spouse of the performer of song Et Moi, Et Moi, Et Moi is Charles Aznavour.",
+        "answer": [
+            "Françoise Hardy"
+        ]
+    },
+    {
+        "output": "Jean-Claude Lauzon.",
+        "answer": [
+            "Charles Wheatstone"
+        ]
+    },
+    {
+        "output": "Passage 6.",
+        "answer": [
+            "Washington"
+        ]
+    },
+    {
+        "output": "Our Agent Tiger.",
+        "answer": [
+            "London Melody"
+        ]
+    },
+    {
+        "output": "Fernando Fernández de Ovando.",
+        "answer": [
+            "Flaín Muñoz"
+        ]
+    },
+    {
+        "output": "The Trouble With Cali is not mentioned in the given passages.",
+        "answer": [
+            "Mira Sorvino"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Denmark.",
+        "answer": [
+            "Norway"
+        ]
+    },
+    {
+        "output": "The cause of death of Heonjong Of Goryeo's father is not mentioned in the given passages.",
+        "answer": [
+            "illness"
+        ]
+    },
+    {
+        "output": "Mary Of Woodstock's mother died in Cumberland.",
+        "answer": [
+            "Harby"
+        ]
+    },
+    {
+        "output": "Dante Lam won the Hundred Flowers Award for Best Director for his work on Beast Stalker in 2008.",
+        "answer": [
+            "Hong Kong Film Award for Best Director"
+        ]
+    },
+    {
+        "output": "The date of death of the director of film Nallavan Vazhvan is not given in the passages provided.",
+        "answer": [
+            "3 September 1992"
+        ]
+    },
+    {
+        "output": "Frederick Cleveland Morgan.",
+        "answer": [
+            "Nathaniel Mclenaghan"
+        ]
+    },
+    {
+        "output": "Tisch School of the Arts.",
+        "answer": [
+            "Tisch"
+        ]
+    },
+    {
+        "output": "Cuchillos De Fuego.",
+        "answer": [
+            "Cuchillos De Fuego"
+        ]
+    },
+    {
+        "output": "Moment Of Danger.",
+        "answer": [
+            "Moment Of Danger"
+        ]
+    },
+    {
+        "output": "Rock Street Journal.",
+        "answer": [
+            "De As"
+        ]
+    },
+    {
+        "output": "The Piper's Price.",
+        "answer": [
+            "The Piper'S Price"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "1753",
+        "answer": [
+            "13 March 1753"
+        ]
+    },
+    {
+        "output": "J. Lee Thompson directed both True To The Navy and No Trees In The Street. He died later.",
+        "answer": [
+            "No Trees In The Street"
+        ]
+    },
+    {
+        "output": "The director of film Ninamaninja Kalpadukal was born in Methala, Near Kalady in Kerala.",
+        "answer": [
+            "Methala"
+        ]
+    },
+    {
+        "output": "Alkohol.",
+        "answer": [
+            "Alkohol"
+        ]
+    },
+    {
+        "output": "Many Tanks Mr. Atkins",
+        "answer": [
+            "Do Musafir"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "The composer of film Consenting Adults (1992 Film) was born in New York City.",
+        "answer": [
+            "New York"
+        ]
+    },
+    {
+        "output": "Tiger in the Smoke.",
+        "answer": [
+            "Contragolpe"
+        ]
+    },
+    {
+        "output": "The given passages do not mention the place of death of the director of film Jail Yatra.",
+        "answer": [
+            "Mumbai"
+        ]
+    },
+    {
+        "output": "F The Prom.",
+        "answer": [
+            "The Comedians Of Comedy"
+        ]
+    },
+    {
+        "output": "Waiting For The Clouds.",
+        "answer": [
+            "Tombstone Rashomon"
+        ]
+    },
+    {
+        "output": "Dhuen Ki Lakeer",
+        "answer": [
+            "Dhuen Ki Lakeer"
+        ]
+    },
+    {
+        "output": "Perdón, Viejita.",
+        "answer": [
+            "Perdón, Viejita"
+        ]
+    },
+    {
+        "output": "The director of film American Gothic (2007 Film) graduated from the University of Wisconsin-Madison.",
+        "answer": [
+            "University of Wisconsin"
+        ]
+    },
+    {
+        "output": "Dudley Russell was born first.",
+        "answer": [
+            "Dudley Russell"
+        ]
+    },
+    {
+        "output": "Vytautas Straižys.",
+        "answer": [
+            "Mirjam Polkunen"
+        ]
+    },
+    {
+        "output": "In the United States.",
+        "answer": [
+            "Saint Petersburg"
+        ]
+    },
+    {
+        "output": "The director of film The Dinner (2013 Film) was born in the Netherlands.",
+        "answer": [
+            "Eindhoven"
+        ]
+    },
+    {
+        "output": "Women's Suffrage Journal.",
+        "answer": [
+            "Women'S Suffrage Journal"
+        ]
+    },
+    {
+        "output": "The given passages do not provide enough information to answer the question.",
+        "answer": [
+            "Fairmont, West Virginia"
+        ]
+    },
+    {
+        "output": "Mayor Muthanna.",
+        "answer": [
+            "The Market Of Souls"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Sidi Bou Said.",
+        "answer": [
+            "Morocco"
+        ]
+    },
+    {
+        "output": "The passage does not mention the place of death of Enheduanna's father.",
+        "answer": [
+            "Akkadian empire"
+        ]
+    },
+    {
+        "output": "Tarzan The Magnificent.",
+        "answer": [
+            "Tarzan The Magnificent"
+        ]
+    },
+    {
+        "output": "The Man Is Armed.",
+        "answer": [
+            "Wolf Warrior"
+        ]
+    },
+    {
+        "output": "Kaya Alp.",
+        "answer": [
+            "Trịnh Bính"
+        ]
+    },
+    {
+        "output": "The director of film Hellcats Of The Navy was born in Australia.",
+        "answer": [
+            "Gura Humorului"
+        ]
+    },
+    {
+        "output": "Los Angeles, California, United States.",
+        "answer": [
+            "Crenshaw"
+        ]
+    },
+    {
+        "output": "Winter Sleepers.",
+        "answer": [
+            "Winter Sleepers"
+        ]
+    },
+    {
+        "output": "The paternal grandfather of Bruno II von Berg is not mentioned in the given passages.",
+        "answer": [
+            "Adolf II of Berg"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "Germany",
+        "answer": [
+            "Mülheim an der Ruhr"
+        ]
+    },
+    {
+        "output": "The Bag Man.",
+        "answer": [
+            "Una Prostituta Al Servizio Del Pubblico E In Regola Con Le Leggi Dello Stato"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "John Mackay (poet)",
+        "answer": [
+            "Mongkut"
+        ]
+    },
+    {
+        "output": "The given passages do not provide enough information to answer the question.",
+        "answer": [
+            "Lady Frances Manners"
+        ]
+    },
+    {
+        "output": "The director of film L'Arbre, Le Maire Et La Médiathèque works at the Sam Spiegel Film and Television School.",
+        "answer": [
+            "Cahiers du cinéma"
+        ]
+    },
+    {
+        "output": "Mexico.",
+        "answer": [
+            "Newport, Wales"
+        ]
+    },
+    {
+        "output": "Gordonsville, Virginia.",
+        "answer": [
+            "Gordonsville, Virginia"
+        ]
+    },
+    {
+        "output": "Romania.",
+        "answer": [
+            "Mangalia"
+        ]
+    },
+    {
+        "output": "Patrice Robitaille is the father of Pier-Luc Funk, the director of the film A No-Hit No-Run Summer.",
+        "answer": [
+            "Félix Leclerc"
+        ]
+    },
+    {
+        "output": "Henri Ii, Duke Of Nemours's paternal grandfather is not mentioned in the given passages.",
+        "answer": [
+            "Jacques"
+        ]
+    },
+    {
+        "output": "Ludwig von Westphalen.",
+        "answer": [
+            "Géza"
+        ]
+    },
+    {
+        "output": "Thuya",
+        "answer": [
+            "Yi Jiang"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific date of death for Maria Isabel De Alcântara, Countess Of Iguaçu's mother.",
+        "answer": [
+            "November 3, 1867"
+        ]
+    },
+    {
+        "output": "The spouse of the director of film Rodney Steps In is not mentioned in the given passages.",
+        "answer": [
+            "Ivy Duke"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific date for Frances Vane, Marchioness of Londonderry's father's death.",
+        "answer": [
+            "1 August 1813"
+        ]
+    },
+    {
+        "output": "The Yellow Teddy Bears.",
+        "answer": [
+            "Season Of Strangers"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "The passage does not provide enough information to determine who was born first.",
+        "answer": [
+            "Antoine Casavant"
+        ]
+    },
+    {
+        "output": "Brijlal Nehru graduated from the University of Vienna.",
+        "answer": [
+            "Oxford"
+        ]
+    },
+    {
+        "output": "Sir Hew Dalrymple, 3rd Baronet.",
+        "answer": [
+            "Sir Hew Dalrymple, 2nd Baronet"
+        ]
+    },
+    {
+        "output": "Khud-Daar.",
+        "answer": [
+            "Murderer In The Fog"
+        ]
+    },
+    {
+        "output": "The spouse of the director of film Soldier (1998 American Film) is Milla Jovovich.",
+        "answer": [
+            "Milla"
+        ]
+    },
+    {
+        "output": "Mary of Enghien died in 1311.",
+        "answer": [
+            "Lecce"
+        ]
+    },
+    {
+        "output": "The director of film Lady Magdalene's, J. Neil Schulman, won three film-festival awards: \"Best Cutting Edge Film",
+        "answer": [
+            "Prometheus Award"
+        ]
+    },
+    {
+        "output": "German.",
+        "answer": [
+            "Duchy of Saxe-Gotha-Altenburg"
+        ]
+    },
+    {
+        "output": "The Longshots.",
+        "answer": [
+            "Station For Two"
+        ]
+    },
+    {
+        "output": "The given passages do not provide enough information to determine the place of birth of Aleksey Greig's father.",
+        "answer": [
+            "Inverkeithing"
+        ]
+    },
+    {
+        "output": "The given passages do not provide a clear answer to this question.",
+        "answer": [
+            "1234"
+        ]
+    },
+    {
+        "output": "Montagu Bertie, 2nd Earl of Lindsey died in 1666.",
+        "answer": [
+            "25 July 1666"
+        ]
+    },
+    {
+        "output": "The Wind's Fierce.",
+        "answer": [
+            "The Wind'S Fierce"
+        ]
+    },
+    {
+        "output": "The director of Port Of Seven Seas died by suicide.",
+        "answer": [
+            "drowning"
+        ]
+    },
+    {
+        "output": "Ajman.",
+        "answer": [
+            "Abu Dhabi"
+        ]
+    },
+    {
+        "output": "Prenzlau, Germany.",
+        "answer": [
+            "Prenzlau"
+        ]
+    },
+    {
+        "output": "Poland",
+        "answer": [
+            "Poznań"
+        ]
+    },
+    {
+        "output": "Forlì.",
+        "answer": [
+            "Italy"
+        ]
+    },
+    {
+        "output": "The given passages do not provide enough information to determine where the husband of Maude Smith was born.",
+        "answer": [
+            "Toronto, Ontario"
+        ]
+    },
+    {
+        "output": "The Third Kiss was released earlier.",
+        "answer": [
+            "Forbidden Daughters"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Bogdan Țăruș.",
+        "answer": [
+            "Bogdan Țăruș"
+        ]
+    },
+    {
+        "output": "Folgore Division.",
+        "answer": [
+            "Sandflow"
+        ]
+    },
+    {
+        "output": "From Corleone To Brooklyn.",
+        "answer": [
+            "From Corleone To Brooklyn"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "The composer of song Gretchen Am Spinnrade was Franz Schubert. He died in Vienna, Austria.",
+        "answer": [
+            "Vienna"
+        ]
+    },
+    {
+        "output": "Hugh Evelyn Wortham.",
+        "answer": [
+            "Johnny Ekström"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "English",
+        "answer": [
+            "England"
+        ]
+    },
+    {
+        "output": "The cause of death of director of film I Will, I Will... For Now is not mentioned in the given passages.",
+        "answer": [
+            "Parkinson"
+        ]
+    },
+    {
+        "output": "Maurice De Berkeley, 4th Baron Berkeley's maternal grandfather is Thomas De Berkeley, 5th Baron Berkeley.",
+        "answer": [
+            "Roger Mortimer, 1st Earl of March"
+        ]
+    },
+    {
+        "output": "Albertus Seba.",
+        "answer": [
+            "Albertus Seba"
+        ]
+    },
+    {
+        "output": "The maternal grandfather of Marie Louise, Duchess Of Parma is Charles V, Holy Roman Emperor.",
+        "answer": [
+            "Ferdinand I of the Two Sicilies"
+        ]
+    },
+    {
+        "output": "Prince Of Arcadia.",
+        "answer": [
+            "Prince Of Arcadia"
+        ]
+    },
+    {
+        "output": "Italy.",
+        "answer": [
+            "Milan"
+        ]
+    },
+    {
+        "output": "Herbert Maisch died in Köln.",
+        "answer": [
+            "Köln"
+        ]
+    }
+]

data/results/longbench/test/generation/hotpotqa.json

@@ -0,0 +1,1202 @@
+[
+    {
+        "output": "Miller v. California.",
+        "answer": [
+            "Miller v. California"
+        ]
+    },
+    {
+        "output": "Charles L. Clifford.",
+        "answer": [
+            "Charles L. Clifford"
+        ]
+    },
+    {
+        "output": "Flint",
+        "answer": [
+            "Norman"
+        ]
+    },
+    {
+        "output": "Jupiter",
+        "answer": [
+            "Sun"
+        ]
+    },
+    {
+        "output": "On film.",
+        "answer": [
+            "erotic thriller film"
+        ]
+    },
+    {
+        "output": "Parliament",
+        "answer": [
+            "Parliament"
+        ]
+    },
+    {
+        "output": "Kumbakonam",
+        "answer": [
+            "Tharangambadi"
+        ]
+    },
+    {
+        "output": "Pleiospilos.",
+        "answer": [
+            "Pleiospilos"
+        ]
+    },
+    {
+        "output": "2013",
+        "answer": [
+            "2013"
+        ]
+    },
+    {
+        "output": "Noelle Scaggs",
+        "answer": [
+            "The Rebirth"
+        ]
+    },
+    {
+        "output": "Ashanti was born on October 13, 1980.",
+        "answer": [
+            "October 13, 1980"
+        ]
+    },
+    {
+        "output": "NPO 1 Extra was founded by Han Peekel.",
+        "answer": [
+            "Betty Cohen"
+        ]
+    },
+    {
+        "output": "Bette Davis.",
+        "answer": [
+            "Mary Astor"
+        ]
+    },
+    {
+        "output": "Finally includes the song that peaked at number five on the US Billboard Hot 100 in January 1992.",
+        "answer": [
+            "number five"
+        ]
+    },
+    {
+        "output": "The forests of Mara and Mondrem share a common deer species with the forests of Cheshire, England.",
+        "answer": [
+            "New Zealand, Canada, the United States, the Falkland Islands, and Peru."
+        ]
+    },
+    {
+        "output": "2000 Summer Olympics.",
+        "answer": [
+            "2000 Summer Olympic Games"
+        ]
+    },
+    {
+        "output": "Claudio López",
+        "answer": [
+            "Claudio Javier López"
+        ]
+    },
+    {
+        "output": "Ronald Reagan",
+        "answer": [
+            "George H. W. Bush"
+        ]
+    },
+    {
+        "output": "\"Grace Kelly\" by Mika.",
+        "answer": [
+            "\"Talk That Talk\""
+        ]
+    },
+    {
+        "output": "George Harrison",
+        "answer": [
+            "Bob Dylan"
+        ]
+    },
+    {
+        "output": "SNICK",
+        "answer": [
+            "Cartoon Cartoon Fridays"
+        ]
+    },
+    {
+        "output": "Blacktown, New South Wales, Australia.",
+        "answer": [
+            "on the Cumberland Plain"
+        ]
+    },
+    {
+        "output": "Fire Island and Long Island.",
+        "answer": [
+            "Long Island"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "PewDiePie"
+        ]
+    },
+    {
+        "output": "It's Always Sunny in Philadelphia",
+        "answer": [
+            "It's Always Sunny in Philadelphia"
+        ]
+    },
+    {
+        "output": "Blue Valley West High School",
+        "answer": [
+            "#364"
+        ]
+    },
+    {
+        "output": "The ribosome.",
+        "answer": [
+            "Ribosomes"
+        ]
+    },
+    {
+        "output": "Pistacia.",
+        "answer": [
+            "Dracula"
+        ]
+    },
+    {
+        "output": "Band-e-Amir Dragons is named after the Band-e-Amir lakes in Bamyan Province, Afghanistan.",
+        "answer": [
+            "Band-e Amir National Park"
+        ]
+    },
+    {
+        "output": "USC",
+        "answer": [
+            "University of Southern California"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific number of citizens for Yau Ma Tei North.",
+        "answer": [
+            "7.2 million"
+        ]
+    },
+    {
+        "output": "A123 Systems",
+        "answer": [
+            "A123 Systems, LLC"
+        ]
+    },
+    {
+        "output": "The population of the city at which the transmitter of WLLZ-LP was placed is not mentioned in the given passages.",
+        "answer": [
+            "143,372"
+        ]
+    },
+    {
+        "output": "Taoiseach",
+        "answer": [
+            "Taoiseach of Ireland"
+        ]
+    },
+    {
+        "output": "Troy, Montana.",
+        "answer": [
+            "Troy, Montana"
+        ]
+    },
+    {
+        "output": "Mika Häkkinen",
+        "answer": [
+            "Mika Häkkinen"
+        ]
+    },
+    {
+        "output": "The Hunger Games: Catching Fire.",
+        "answer": [
+            "Constantine"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "George Balanchine"
+        ]
+    },
+    {
+        "output": "North American Light and Power Company.",
+        "answer": [
+            "Xcel Energy Inc."
+        ]
+    },
+    {
+        "output": "Summer Magic.",
+        "answer": [
+            "Summer Magic"
+        ]
+    },
+    {
+        "output": "The physicist who is responsible for identifying the Rabi cycle is Isidor Isaac Rabi. He won the Nobel Prize in Physics in 19",
+        "answer": [
+            "Nobel Prize"
+        ]
+    },
+    {
+        "output": "Stop-motion animation",
+        "answer": [
+            "seasonal television specials, particularly its work in stop motion animation"
+        ]
+    },
+    {
+        "output": "Wendy Craig.",
+        "answer": [
+            "Dame Eileen June Atkins"
+        ]
+    },
+    {
+        "output": "Iran.",
+        "answer": [
+            "Iran"
+        ]
+    },
+    {
+        "output": "Huey P. Newton",
+        "answer": [
+            "Hakim Abdullah Jamal"
+        ]
+    },
+    {
+        "output": "Keith Morris.",
+        "answer": [
+            "Keith Morris"
+        ]
+    },
+    {
+        "output": "YIVO",
+        "answer": [
+            "YIVO"
+        ]
+    },
+    {
+        "output": "The plaintiff in the 1892 Barbed Wire Patent Case was born on March 9, 1826.",
+        "answer": [
+            "January 18, 1813"
+        ]
+    },
+    {
+        "output": "Himalchuli is higher.",
+        "answer": [
+            "Himalchuli has three main peaks: East (7893 m), West (7540 m) and North (7371 m)"
+        ]
+    },
+    {
+        "output": "The place in Canada where François Beaucourt (1740-1794) was mainly active was acquired by the British in 17",
+        "answer": [
+            "1763"
+        ]
+    },
+    {
+        "output": "Pope John X.",
+        "answer": [
+            "Pope John X"
+        ]
+    },
+    {
+        "output": "Manchester United.",
+        "answer": [
+            "Manchester United"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "8,530",
+        "answer": [
+            "1,693"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Australian Supercars Championship"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "Mike Leach currently coaches at Washington State.",
+        "answer": [
+            "Washington State"
+        ]
+    },
+    {
+        "output": "Floyd Casey Stadium",
+        "answer": [
+            "Floyd Casey Stadium"
+        ]
+    },
+    {
+        "output": "2010",
+        "answer": [
+            "2010"
+        ]
+    },
+    {
+        "output": "Elephants are not directly connected to Gajabrishta. The passage only mentions the word \"Gaja\" which means elephant in S",
+        "answer": [
+            "Sanskrit"
+        ]
+    },
+    {
+        "output": "British",
+        "answer": [
+            "English"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "The animated series ended after one season in November 2002.",
+        "answer": [
+            "November"
+        ]
+    },
+    {
+        "output": "Jennifer Grey.",
+        "answer": [
+            "Shari Shattuck"
+        ]
+    },
+    {
+        "output": "Cat Stevens",
+        "answer": [
+            "British"
+        ]
+    },
+    {
+        "output": "Vernon L. Smith",
+        "answer": [
+            "Vernon Smith"
+        ]
+    },
+    {
+        "output": "Deftones",
+        "answer": [
+            "Deftones"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific population for the town where The Stelljes House is located.",
+        "answer": [
+            "810"
+        ]
+    },
+    {
+        "output": "India",
+        "answer": [
+            "India"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Erik Watts' father was born on October 15, 1733.",
+        "answer": [
+            "May 5, 1939"
+        ]
+    },
+    {
+        "output": "Philip K. Dick",
+        "answer": [
+            "Philip K. Dick"
+        ]
+    },
+    {
+        "output": "The Roud Folk Song Index for the nursery rhyme inspiring What Are Little Girls Made Of is 7734.",
+        "answer": [
+            "821"
+        ]
+    },
+    {
+        "output": "Capital Cities.",
+        "answer": [
+            "Capital Cities"
+        ]
+    },
+    {
+        "output": "Morgan Llywelyn.",
+        "answer": [
+            "Morgan Llywelyn"
+        ]
+    },
+    {
+        "output": "18",
+        "answer": [
+            "14"
+        ]
+    },
+    {
+        "output": "Big 12",
+        "answer": [
+            "Big 12 Conference"
+        ]
+    },
+    {
+        "output": "The six Western European territories where Celtic languages or cultural traits have survived are Brittany, Cornwall, Galicia, Ireland, Scotland",
+        "answer": [
+            "Brittany, Cornwall, Ireland, Isle of Man, Scotland and Wales."
+        ]
+    },
+    {
+        "output": "The SR connector is a type of RF connector used for terminating a coaxial cable. It is similar to the BNC and MH",
+        "answer": [
+            "coaxial"
+        ]
+    },
+    {
+        "output": "Wicked Twister is farther north.",
+        "answer": [
+            "Wicked Twister"
+        ]
+    },
+    {
+        "output": "Lev Yilmaz.",
+        "answer": [
+            "Levni Yilmaz"
+        ]
+    },
+    {
+        "output": "The gunman of the hostage crisis that Chris Reason was awarded the Graham Perkin Australian Journalist of the Year Award for his coverage of was Man Har",
+        "answer": [
+            "Man Haron"
+        ]
+    },
+    {
+        "output": "Hawaii",
+        "answer": [
+            "Hawaii"
+        ]
+    },
+    {
+        "output": "100 meters",
+        "answer": [
+            "100 metres"
+        ]
+    },
+    {
+        "output": "The CEO of the football team based in Denver, Colorado stepped down in 2014 due to complications with Alzheimer's disease",
+        "answer": [
+            "due to the onset and progression of Alzheimer's disease"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Leucippus",
+        "answer": [
+            "Leucippus"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "Hollywood, Florida"
+        ]
+    },
+    {
+        "output": "Both 277 Park Avenue and 126 Madison Avenue are art deco skyscrapers.",
+        "answer": [
+            "building"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "1895"
+        ]
+    },
+    {
+        "output": "The answer is the Pac-12 Conference.",
+        "answer": [
+            "Pac-12 Conference"
+        ]
+    },
+    {
+        "output": "The Krypton Factor.",
+        "answer": [
+            "7pm"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "Both Marge Piercy and Richard Aldington were writers.",
+        "answer": [
+            "poet"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "17,000",
+        "answer": [
+            "43,000 marchers"
+        ]
+    },
+    {
+        "output": "The airline company that operated United Express Flight 3411 on April 9, 2017 on behalf of United Express is",
+        "answer": [
+            "Indianapolis, Indiana"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "1909"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Bill McCutcheon"
+        ]
+    },
+    {
+        "output": "Field Marshal Lord Gort",
+        "answer": [
+            "Lord Gort"
+        ]
+    },
+    {
+        "output": "Des Moines, Iowa.",
+        "answer": [
+            "Polk County"
+        ]
+    },
+    {
+        "output": "James II of England.",
+        "answer": [
+            "King James II of England"
+        ]
+    },
+    {
+        "output": "\"Behind Blue Eyes\"",
+        "answer": [
+            "Tommy"
+        ]
+    },
+    {
+        "output": "The American electronic music duo from Orlando, Florida, that Angelspit toured with in the United States is Blood on the Dance Floor. Blood",
+        "answer": [
+            "2006"
+        ]
+    },
+    {
+        "output": "Santería.",
+        "answer": [
+            "West African descendants"
+        ]
+    },
+    {
+        "output": "American",
+        "answer": [
+            "American"
+        ]
+    },
+    {
+        "output": "The answer is Dr. Charles Cullen.",
+        "answer": [
+            "Michael Swango"
+        ]
+    },
+    {
+        "output": "Juan Rulfo.",
+        "answer": [
+            "Juan Rulfo"
+        ]
+    },
+    {
+        "output": "Merck & Co.",
+        "answer": [
+            "American"
+        ]
+    },
+    {
+        "output": "The author of Cock and Bull was born in 1709.",
+        "answer": [
+            "1961"
+        ]
+    },
+    {
+        "output": "Governor",
+        "answer": [
+            "United States Secretary of the Interior"
+        ]
+    },
+    {
+        "output": "Through the Looking-Glass.",
+        "answer": [
+            "Alice's Adventures in Wonderland"
+        ]
+    },
+    {
+        "output": "Vienna",
+        "answer": [
+            "Vienna"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Albert Park",
+        "answer": [
+            "Albert Park"
+        ]
+    },
+    {
+        "output": "Actors",
+        "answer": [
+            "director"
+        ]
+    },
+    {
+        "output": "Corbin, Kentucky.",
+        "answer": [
+            "Jeffersontown, Kentucky"
+        ]
+    },
+    {
+        "output": "Richard Eichberg directed the film where Susanne Pollatschek voiced Olivia Flaversham.",
+        "answer": [
+            "Burny Mattinson, David Michener, and the team of John Musker and Ron Clements"
+        ]
+    },
+    {
+        "output": "WAMC",
+        "answer": [
+            "WAMC"
+        ]
+    },
+    {
+        "output": "Swan Districts",
+        "answer": [
+            "Bassendean"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "duck"
+        ]
+    },
+    {
+        "output": "Mimosa.",
+        "answer": [
+            "Mimosa"
+        ]
+    },
+    {
+        "output": "Disappearances and strange happenings.",
+        "answer": [
+            "alleged paranormal phenomena"
+        ]
+    },
+    {
+        "output": "Coca-Cola FEMSA",
+        "answer": [
+            "Fomento Económico Mexicano"
+        ]
+    },
+    {
+        "output": "Ellie Kemper",
+        "answer": [
+            "Ellie Kemper"
+        ]
+    },
+    {
+        "output": "Yes, Yut and Tsuro are both board games.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Umina Beach",
+        "answer": [
+            "Umina Beach, New South Wales"
+        ]
+    },
+    {
+        "output": "The answer is Cebu City. The Robinsons Galleria Cebu is located in Cebu City and is the 4th largest sho",
+        "answer": [
+            "Metro Manila"
+        ]
+    },
+    {
+        "output": "Both Sandra Pizzarello and Doctorate have a PhD.",
+        "answer": [
+            "University"
+        ]
+    },
+    {
+        "output": "Suining",
+        "answer": [
+            "In 2002, Suining had a population of 658,798."
+        ]
+    },
+    {
+        "output": "Both Yameen and Activision have collaborated on concept albums.",
+        "answer": [
+            "video game"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "Russian.",
+        "answer": [
+            "French mathematician and physicist"
+        ]
+    },
+    {
+        "output": "Elvis' Christmas Album",
+        "answer": [
+            "Elvis' Christmas Album"
+        ]
+    },
+    {
+        "output": "The passage does not mention a battle codenamed Camp Courtney. The passage mentions a battle codenamed Tsukiji.",
+        "answer": [
+            "Operation Iceberg"
+        ]
+    },
+    {
+        "output": "Logar Province",
+        "answer": [
+            "Puli Alam"
+        ]
+    },
+    {
+        "output": "Alain Prost",
+        "answer": [
+            "BBC Formula One"
+        ]
+    },
+    {
+        "output": "Socrates",
+        "answer": [
+            "Plato"
+        ]
+    },
+    {
+        "output": "The host of the American survival skills reality show, The Island, is originally from the United Kingdom.",
+        "answer": [
+            "Northern Ireland"
+        ]
+    },
+    {
+        "output": "Jerry Garcia.",
+        "answer": [
+            "Joe Gooch"
+        ]
+    },
+    {
+        "output": "Lavinia Greenlaw and Nâzım Hikmet are both poets.",
+        "answer": [
+            "poet and novelist"
+        ]
+    },
+    {
+        "output": "O",
+        "answer": [
+            "O"
+        ]
+    },
+    {
+        "output": "Wittenberg University",
+        "answer": [
+            "West Lafayette, Indiana"
+        ]
+    },
+    {
+        "output": "John Locke",
+        "answer": [
+            "Father of Liberalism"
+        ]
+    },
+    {
+        "output": "35",
+        "answer": [
+            "35 people"
+        ]
+    },
+    {
+        "output": "Charlotte Brontë",
+        "answer": [
+            "\"Currer Bell\""
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "2009",
+        "answer": [
+            "2011"
+        ]
+    },
+    {
+        "output": "Cortina d'Ampezzo, Italy.",
+        "answer": [
+            "the Veneto region of Northern Italy"
+        ]
+    },
+    {
+        "output": "The club was formed in 1970.",
+        "answer": [
+            "1919"
+        ]
+    },
+    {
+        "output": "The Arab Kingdom of Syria surrendered to French forces on 25 July 1920.",
+        "answer": [
+            "French"
+        ]
+    },
+    {
+        "output": "The answer is:",
+        "answer": [
+            "University of Vienna"
+        ]
+    },
+    {
+        "output": "Brian Stokes Mitchell",
+        "answer": [
+            "Brian Stokes Mitchell"
+        ]
+    },
+    {
+        "output": "Netflix",
+        "answer": [
+            "Netflix"
+        ]
+    },
+    {
+        "output": "No, Clans was created by Leo Colovini while Drunter und Drüber was created by Klaus Teuber.",
+        "answer": [
+            "Drunter und Drüber is a multiplayer board game invented by Klaus Teuber"
+        ]
+    },
+    {
+        "output": "Louisville, Kentucky.",
+        "answer": [
+            "Louisville"
+        ]
+    },
+    {
+        "output": "Sonic Mania.",
+        "answer": [
+            "Sonic Mania"
+        ]
+    },
+    {
+        "output": "Mark Donohue",
+        "answer": [
+            "Mark Neary Donohue Jr."
+        ]
+    },
+    {
+        "output": "Actor",
+        "answer": [
+            "an acting career"
+        ]
+    },
+    {
+        "output": "The first film scored by the man who composed the soundtrack for Udhayam NH4 was Gentleman (1993).",
+        "answer": [
+            "Veyyil"
+        ]
+    },
+    {
+        "output": "The Kentucky Derby.",
+        "answer": [
+            "Saturday"
+        ]
+    },
+    {
+        "output": "Days of Our Lives",
+        "answer": [
+            "Neighbours"
+        ]
+    },
+    {
+        "output": "Bill Miner.",
+        "answer": [
+            "Bill Miner"
+        ]
+    },
+    {
+        "output": "Saginaw",
+        "answer": [
+            "Chippewa County"
+        ]
+    },
+    {
+        "output": "The Androscoggin Bank Colisée can seat 5,100 people.",
+        "answer": [
+            "3,677 seated"
+        ]
+    },
+    {
+        "output": "The ingredients for She-crab soup are likely to come from the Atlantic Ocean.",
+        "answer": [
+            "Atlantic Ocean"
+        ]
+    },
+    {
+        "output": "Both Raj Kapoor and Mike Cahill are filmmakers.",
+        "answer": [
+            "director"
+        ]
+    },
+    {
+        "output": "Start",
+        "answer": [
+            "IK Start"
+        ]
+    },
+    {
+        "output": "Green and yellow.",
+        "answer": [
+            "green and yellow"
+        ]
+    },
+    {
+        "output": "Outlander",
+        "answer": [
+            "Claire is a married World War II nurse"
+        ]
+    },
+    {
+        "output": "Douglas Murray serves as associate director of the Henry Jackson Society.",
+        "answer": [
+            "Henry Jackson Society"
+        ]
+    },
+    {
+        "output": "The city was also known as the \"Garden of Eden\" during the Abbasid Caliphate.",
+        "answer": [
+            "the City of Peace"
+        ]
+    },
+    {
+        "output": "The film was directed by Terence Davies.",
+        "answer": [
+            "Martin Scorsese"
+        ]
+    },
+    {
+        "output": "Michael Tippett.",
+        "answer": [
+            "Michael Kemp Tippett"
+        ]
+    },
+    {
+        "output": "Babylon",
+        "answer": [
+            "Babylon"
+        ]
+    },
+    {
+        "output": "Ten Walls",
+        "answer": [
+            "Ten Walls"
+        ]
+    },
+    {
+        "output": "John Paesano wrote the music for the film How to Train Your Dragon.",
+        "answer": [
+            "Dragons: Riders of Berk"
+        ]
+    },
+    {
+        "output": "1975",
+        "answer": [
+            "1978"
+        ]
+    },
+    {
+        "output": "The lowest vocal range in Cosmos was C2.",
+        "answer": [
+            "Jānis Strazdiņš"
+        ]
+    },
+    {
+        "output": "Novelists",
+        "answer": [
+            "writer"
+        ]
+    },
+    {
+        "output": "The answer is Nanyue.",
+        "answer": [
+            "the Qin dynasty"
+        ]
+    },
+    {
+        "output": "Ole Bull",
+        "answer": [
+            "Baron of Holberg"
+        ]
+    },
+    {
+        "output": "The punter for the Dallas Cowboys in the 1980s played college football for the University of Mississippi.",
+        "answer": [
+            "Arizona State University"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "yes"
+        ]
+    },
+    {
+        "output": "Matthew Good Band is still together.",
+        "answer": [
+            "Switchfoot"
+        ]
+    },
+    {
+        "output": "No. Portland International Airport is located in Oregon, while Gerald R. Ford International Airport is located in Michigan.",
+        "answer": [
+            "no"
+        ]
+    },
+    {
+        "output": "The Quays won more awards.",
+        "answer": [
+            "Brothers Quay"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific length for the river that the Atherton Bridge spans.",
+        "answer": [
+            "37.5"
+        ]
+    },
+    {
+        "output": "The Bangor Daily News is not talking about Sawin Millett. The Bangor Daily News is a newspaper in Bangor, Maine. The pass",
+        "answer": [
+            "Commissioner by the Maine Senate"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "Jodie Prenger"
+        ]
+    },
+    {
+        "output": "The River Thames.",
+        "answer": [
+            "Thames"
+        ]
+    },
+    {
+        "output": "Allure.",
+        "answer": [
+            "Allure"
+        ]
+    },
+    {
+        "output": "Jaleel White",
+        "answer": [
+            "Jaleel Ahmad White"
+        ]
+    },
+    {
+        "output": "Lake Wallace",
+        "answer": [
+            "Lake Wallace"
+        ]
+    },
+    {
+        "output": "The passage does not mention the number of records sold by the singer who sang \"Four Seasons of Love\".",
+        "answer": [
+            "140 million"
+        ]
+    },
+    {
+        "output": "Joe Wilkinson and Diane Morgan's double act Two Episodes of Mash.",
+        "answer": [
+            "the deadpan sketch group"
+        ]
+    },
+    {
+        "output": "2021",
+        "answer": [
+            "1998"
+        ]
+    },
+    {
+        "output": "Henrik Ibsen.",
+        "answer": [
+            "William"
+        ]
+    },
+    {
+        "output": "John Byrne",
+        "answer": [
+            "Gerald \"Jerry\" Joseph Bingham, Jr."
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "no"
+        ]
+    }
+]

data/results/longbench/test/generation/multifieldqa_en.json

@@ -0,0 +1,902 @@
+[
+    {
+        "output": "South West Ultras",
+        "answer": [
+            "South West Ultras fan club."
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "No, it is not necessary."
+        ]
+    },
+    {
+        "output": "STM/STS and ab-initio simulations.",
+        "answer": [
+            "Low temperature scanning tunneling microscopy and spectroscopy (STM/STS)."
+        ]
+    },
+    {
+        "output": "The purpose of an ICD is to diagnose and treat heart arrhythmias.",
+        "answer": [
+            "Implantable Cardioverter Defibrillator (ICD) is a surgically implanted electronic device to treat life-threatening heartbeat irregularities."
+        ]
+    },
+    {
+        "output": "To create a conical shape when the sides are bent and sloped to form the fuselage box section.",
+        "answer": [
+            "The sides of the fuselage are sloped to create a conical section when the fuselage is formed."
+        ]
+    },
+    {
+        "output": "The proximity of superconductivity tends to suppress the Kondo effect.",
+        "answer": [
+            "It tends to suppress the Kondo effect."
+        ]
+    },
+    {
+        "output": "Electricity is used in a wide range of applications including transportation, heating, lighting, communications, and computation.",
+        "answer": [
+            "Electricity is used for transport, heating, lighting, communications, and computation."
+        ]
+    },
+    {
+        "output": "Vice Admiral",
+        "answer": [
+            "Vice Admiral."
+        ]
+    },
+    {
+        "output": "15–3",
+        "answer": [
+            "They won their first game with a score of 15-3."
+        ]
+    },
+    {
+        "output": "K3, K4, and K5.",
+        "answer": [
+            "Vitamins K3, K4, and K5."
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes, individual molecules of indeno[1,2-a]fluorene can switch between open-shell and closed-shell states by changing their adsorption site on the surface."
+        ]
+    },
+    {
+        "output": "Software development",
+        "answer": [
+            "3-D printing and software development."
+        ]
+    },
+    {
+        "output": "90 micrograms per day.",
+        "answer": [
+            "90 μg for women and 120 μg for men."
+        ]
+    },
+    {
+        "output": "Watt.",
+        "answer": [
+            "Watt, one joule per second."
+        ]
+    },
+    {
+        "output": "Flexibility.",
+        "answer": [
+            "Flexibility."
+        ]
+    },
+    {
+        "output": "Jacob C. Landau.",
+        "answer": [
+            "Jacob C. Landau."
+        ]
+    },
+    {
+        "output": "The main methodology used in the research is based on the information bottleneck and contrastive learning to capture referential complexity and task-specific utility in emergent communication for multi-agent reinforcement learning. The methodology aims to learn a communication lexicon that is independently composed of a set of emer",
+        "answer": [
+            "An unsupervised method based on the information bottleneck and contrastive learning."
+        ]
+    },
+    {
+        "output": "The function beta(r) is determined by solving the differential equation \\begin{equation}",
+        "answer": [
+            "Using the vacuum Einstein equation and the Baez-Bunn form."
+        ]
+    },
+    {
+        "output": "70-75 metres.",
+        "answer": [
+            "The water depth in the Greater Ekofisk Area is 70-75 meters."
+        ]
+    },
+    {
+        "output": "Genuine placebo.",
+        "answer": [
+            "Other toxic products."
+        ]
+    },
+    {
+        "output": "Margaret Way was born in Brisbane, Queensland, Australia and died in Cleveland, Queensland, Australia.",
+        "answer": [
+            "Margaret Way was born in Brisbane and died in Cleveland, Queensland, Australia."
+        ]
+    },
+    {
+        "output": "The size of the chains used in the benchmarking was L = 14.",
+        "answer": [
+            "L = 8 and L = 14."
+        ]
+    },
+    {
+        "output": "5",
+        "answer": [
+            "5."
+        ]
+    },
+    {
+        "output": "Lasa, Gitastrophe, and Shadoks.",
+        "answer": [
+            "Lasa, Gitastrophe, and Shadoks."
+        ]
+    },
+    {
+        "output": "The court in In re Ferguson concluded that the transformation prong of the Bilski test was not satisfied because the step of informing a patient of the inherent property of a product did not transform the product into a different state or thing.",
+        "answer": [
+            "It required the transformation to be limited to specific data and a visual depiction representing specific objects or substances."
+        ]
+    },
+    {
+        "output": "The transition probability of the environment affects the learning rate in the static agent by determining the rate of environmental transitions during the agent's lifetime. The learning rate is a measure of how quickly the agent learns from experience and adapts to the environment. In the static agent, the learning rate is determined by",
+        "answer": [
+            "As the transition probability increases, the learning rate initially rises and then declines."
+        ]
+    },
+    {
+        "output": "Molecular ultracold plasmas.",
+        "answer": [
+            "A subset that form via kinetic rate processes from state-selected Rydberg gases."
+        ]
+    },
+    {
+        "output": "Wearable sensors and ambient sensors.",
+        "answer": [
+            "Wearable sensors."
+        ]
+    },
+    {
+        "output": "Fairness.",
+        "answer": [
+            "Fairness."
+        ]
+    },
+    {
+        "output": "Mary told the disciples that she had seen the Lord.",
+        "answer": [
+            "\"I have seen the Lord.\"."
+        ]
+    },
+    {
+        "output": "The future direction mentioned in the conclusion is to investigate the efficacy of meta-information on other medical tasks, such as radiology report generation.",
+        "answer": [
+            "Verifying other meta-information such as patient's gender, age, race, etc."
+        ]
+    },
+    {
+        "output": "The authors used simulated and real data.",
+        "answer": [
+            "The authors used simulated data and real data from a wireless MISO channel."
+        ]
+    },
+    {
+        "output": "The purpose of the baseline in the layout procedure is to ensure a fair and true fuselage.",
+        "answer": [
+            "The baseline is used as a reference for the mid point of the firewall for the developed side panel."
+        ]
+    },
+    {
+        "output": "Provides cover for the war.",
+        "answer": [
+            "It provides cover for the war and allows supporters of the illegal war to point to it."
+        ]
+    },
+    {
+        "output": "The proposed method does not increase the time required to update the robot's belief with an increase in problem complexity. In fact, the belief update time on Map 2 and Map 3, containing more obstacles, is reduced compared to the first map. This is because the complexity of the Bayesian update should not",
+        "answer": [
+            "The time required to update the belief does not increase with the complexity of the environment."
+        ]
+    },
+    {
+        "output": "The Tevatron Collider Run II started in March 2002 and is expected to continue until the end of this decade.",
+        "answer": [
+            "The Tevatron Collider Run II started in March 2002 and is expected to continue until the end of this decade."
+        ]
+    },
+    {
+        "output": "Four",
+        "answer": [
+            "Four."
+        ]
+    },
+    {
+        "output": "The stability condition for a solution $u$ of $-\\Delta u = f(u)$ is that for all $C^2$ functions $\\psi$ with compact support, $\\int f'(u) \\psi^2 \\le \\int |\\nabla \\psi|^2$. In our setting, this becomes $\\",
+        "answer": [
+            "$\\int f'(u) \\psi^2 \\le \\int | \\nabla \\psi|^2, \\forall \\psi \\in C_c^2$."
+        ]
+    },
+    {
+        "output": "The normalized least mean square (NLMS) algorithm is engaged in the PLMS-PPIC method.",
+        "answer": [
+            "The normalized least mean square (NLMS) algorithm."
+        ]
+    },
+    {
+        "output": "ICAN.",
+        "answer": [
+            "Del Bigtree and his team at ICAN."
+        ]
+    },
+    {
+        "output": "C$_2$H",
+        "answer": [
+            "The focus of the study was on the reactive radical ethynyl (C$_2$H)."
+        ]
+    },
+    {
+        "output": "1964",
+        "answer": [
+            "Weep Not, Child was first published in 1964."
+        ]
+    },
+    {
+        "output": "The maximum velocity of a blob or depletion increases with its amplitude.",
+        "answer": [
+            "The maximum velocity scales with the square root of the amplitude."
+        ]
+    },
+    {
+        "output": "Dendritic spines contain a complex network of proteins that link extracellular signals with the actin cytoskeleton, allowing for control of dendritic spine morphology and number.",
+        "answer": [
+            "They are rich in actin and have been shown to be highly dynamic."
+        ]
+    },
+    {
+        "output": "160,491.",
+        "answer": [
+            "There are 14,520 attendees, including 7,152 chemical scientists, 5,059 students, 1,283 exhibitors, 119 precollege teachers, 573 exposition visitors, and 453 guests."
+        ]
+    },
+    {
+        "output": "He had second thoughts about the decision and wished for the legal system to find an appropriate case for the Court to reexamine Quill and Bellas Hess.",
+        "answer": [
+            "Quill harmed states more than anticipated due to the Internet."
+        ]
+    },
+    {
+        "output": "Environmental variability and task complexity.",
+        "answer": [
+            "Environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity."
+        ]
+    },
+    {
+        "output": "The problem is that the sides bow up from the building surface, forming a \"banana\" shape.",
+        "answer": [
+            "The longerons bow up from the building surface, forming a \"banana\" shape."
+        ]
+    },
+    {
+        "output": "He became a Naval aviator in January 1929.",
+        "answer": [
+            "Goodwin became a Naval aviator in January 1929."
+        ]
+    },
+    {
+        "output": "48V",
+        "answer": [
+            "48V."
+        ]
+    },
+    {
+        "output": "June 1, 1999.",
+        "answer": [
+            "June 1, 1999."
+        ]
+    },
+    {
+        "output": "Mufti-e-Azam-e-Hind received Khilafat in the Qaderi, Chishti, Nakshbandi, Suharwardi, and Madaari Orders.",
+        "answer": [
+            "Mufti-e-Azam-e-Hind received Khilafat in the Qaderi, Chishti, Nakshbandi, Suharwardi, and Madaari Orders."
+        ]
+    },
+    {
+        "output": "Privacy concerns.",
+        "answer": [
+            "Privacy concerns and skepticism about its effectiveness."
+        ]
+    },
+    {
+        "output": "Clutha-Southland",
+        "answer": [
+            "The Wallace electorate."
+        ]
+    },
+    {
+        "output": "30,223",
+        "answer": [
+            "30,223."
+        ]
+    },
+    {
+        "output": "Beginning in 2013.",
+        "answer": [
+            "Margaret Way started self-publishing her books as e-books in 2013."
+        ]
+    },
+    {
+        "output": "23 September.",
+        "answer": [
+            "23 September."
+        ]
+    },
+    {
+        "output": "Long Term Capital Management (LTCM)",
+        "answer": [
+            "Long Term Capital Management (LTCM)."
+        ]
+    },
+    {
+        "output": "The dynamical behavior of the anisotropic order parameter following a quench to the critical point is well described by the Gaussian theory for all the three lattice gas models studied, i.e., driven lattice gas with either constant or random infinite drive and equilibrium lattice gas. In the short-time regime, the",
+        "answer": [
+            "It is well described by the Gaussian theory."
+        ]
+    },
+    {
+        "output": "2x2 meters.",
+        "answer": [
+            "It is recommended to have at least a 2x2 meter space for using the VR headset."
+        ]
+    },
+    {
+        "output": "The PLM with decimation outperforms the other methods in terms of network reconstruction accuracy and has a lower reconstruction error. It is able to infer the network of interaction even when the sample size is close to the number of variables. The PLM with decimation is also able to estimate the total number of",
+        "answer": [
+            "It outperforms mean-field methods and the PLM with $l_2$ regularization in terms of reconstruction error and true positive rate."
+        ]
+    },
+    {
+        "output": "Exegetical, theological, and homiletical.",
+        "answer": [
+            "The three phases are exegetical, theological, and homiletical."
+        ]
+    },
+    {
+        "output": "The text does not mention a specific date for the switch to a sports radio format.",
+        "answer": [
+            "KSTP switched to a sports radio format on February 15, 2010."
+        ]
+    },
+    {
+        "output": "RoBERTa",
+        "answer": [
+            "The best performing model for the Spanish language in Track-1 was Spanish BERT."
+        ]
+    },
+    {
+        "output": "The infall rate in the magnetized model is 2-5 times smaller than in non-magnetized accretion, and the gas density is 2-5 times smaller in the region close to the black hole, where synchrotron radiation emerges.",
+        "answer": [
+            "Infall rate is 2-5 times smaller and gas density is 2-5 times smaller."
+        ]
+    },
+    {
+        "output": "The text does not mention Toby Schindelbeck or the police.",
+        "answer": [
+            "Toby Schindelbeck's observation is that the police say they aren't paid enough to enforce the laws in the streets."
+        ]
+    },
+    {
+        "output": "Peter Denning.",
+        "answer": [
+            "Peter Denning."
+        ]
+    },
+    {
+        "output": "The conduction gap depends on the strain direction in a complex way. It is zero in the case of $\\theta = 0$ and $\\theta = 90^\\circ$ and has peaks at $\\theta \\approx 47^\\circ$ and $133^\\circ$ for tensile",
+        "answer": [
+            "Peaks occur at certain strain directions, while the gap is zero at others."
+        ]
+    },
+    {
+        "output": "7 March 2023",
+        "answer": [
+            "The paper was published on 7 March 2023."
+        ]
+    },
+    {
+        "output": "V +, V 0, V -.",
+        "answer": [
+            "The three subsets are V+, V0, and V-, determined by the Kullback-Leibler information distance."
+        ]
+    },
+    {
+        "output": "Ngotho is fired and the family is forced to move to another section of the country.",
+        "answer": [
+            "After attacking Jacobo at a workers' strike, Ngotho loses his job and Njoroge's family is forced to move."
+        ]
+    },
+    {
+        "output": "Yerevan",
+        "answer": [
+            "The club's headquarters are located on Jivani Street 2 of the Malatia-Sebastia District, Yerevan."
+        ]
+    },
+    {
+        "output": "NFPA and FPSA outperform GMRES and DSA by orders of magnitude in runtime and iteration counts.",
+        "answer": [
+            "NFPA and FPSA greatly outperform GMRES and DSA."
+        ]
+    },
+    {
+        "output": "Lack of data sharing in archaeobotany is due to factors such as time-intensive data collection, publication requirements, commercial confidentiality, limited access to digital archiving resources, and inadequate training in data management and sharing.",
+        "answer": [
+            "Technological limitations, resistance to exposing data to scrutiny, and desire to hold onto data for personal use."
+        ]
+    },
+    {
+        "output": "Physics, biology, social sciences, finance, neuroscience, and optics.",
+        "answer": [
+            "Physics, biology, social sciences, finance, and neuroscience are some fields in which the inverse problem is encountered."
+        ]
+    },
+    {
+        "output": "1000000 km",
+        "answer": [
+            "4.5x10^8 cm."
+        ]
+    },
+    {
+        "output": "VC-10 Squadron",
+        "answer": [
+            "VC-10 Squadron."
+        ]
+    },
+    {
+        "output": "The bigger the receptive field size, the more complete shapes can be reconstructed using DSP.",
+        "answer": [
+            "Bigger receptive field size leads to more successful shape completion."
+        ]
+    },
+    {
+        "output": "The interlayer Berry connection polarizability is significant in the crossed nonlinear dynamical intrinsic Hall effect in twisted bilayers, arising from layer hybridization of electrons by the twisted interlayer coupling. It gives rise to a band geometric origin in the momentum space curl of interlayer Berry",
+        "answer": [
+            "The momentum space curl of the interlayer Berry connection polarizability generates the crossed nonlinear dynamical Hall effect."
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes, the denoiser works for non-Clifford local noise channels."
+        ]
+    },
+    {
+        "output": "Legacies of Losing in American Politics.",
+        "answer": [
+            "Legacies of Losing in American Politics and an expanded edition of The Rhetorical Presidency in the Princeton Classics series."
+        ]
+    },
+    {
+        "output": "The media application determines the context of an event using a content-recognition module.",
+        "answer": [
+            "It uses a content-recognition module or algorithm."
+        ]
+    },
+    {
+        "output": "None of the above.",
+        "answer": [
+            "Sustained viral gene delivery through core-shell fibers and Gene transfer to hemophilia A mice via oral delivery of FVIII-chitosan nanoparticles."
+        ]
+    },
+    {
+        "output": "FC Banants",
+        "answer": [
+            "FC Banants."
+        ]
+    },
+    {
+        "output": "The conclusion of the study was that fatigue was associated with frequent high mercury fish consumption.",
+        "answer": [
+            "The conclusion was that fruit consumption may provide a protective effect for mercury exposure in Amazonian riparians."
+        ]
+    },
+    {
+        "output": "The scoring engine retrieves candidate content items that include the channel category and the other channel attribute. The scoring engine then generates a stream of content from the candidate content items for the channel.",
+        "answer": [
+            "By comparing candidate content items to a model and scoring them."
+        ]
+    },
+    {
+        "output": "2013–14.",
+        "answer": [
+            "In the 2013-2014 season."
+        ]
+    },
+    {
+        "output": "120+",
+        "answer": [
+            "Margaret Way wrote more than 120 novels."
+        ]
+    },
+    {
+        "output": "The proposed approach in this research paper is a probabilistic interpretation of the least-mean-square filter. The resulting algorithm is an adaptable step-size LMS that performs well both in stationary and tracking scenarios. It has fewer free parameters than previous approaches and these parameters have a clear physical meaning. The approach is",
+        "answer": [
+            "This research paper proposed an approach based on approximating the posterior distribution with an isotropic Gaussian distribution."
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes, DUO has 9.57 instances per image while COCO contains 7.7."
+        ]
+    },
+    {
+        "output": "Uncontrolled bleeding.",
+        "answer": [
+            "Symptoms of vitamin K deficiency include anemia, bruising, nosebleeds, bleeding of the gums, and heavy menstrual bleeding in women."
+        ]
+    },
+    {
+        "output": "128 bits",
+        "answer": [
+            "172."
+        ]
+    },
+    {
+        "output": "MDM refers to the centralized control and management of mobile devices such as smartphones and tablets to ensure pre-defined configuration settings and security.",
+        "answer": [
+            "Centralized control of mobile devices and applications."
+        ]
+    },
+    {
+        "output": "BERT and RoBERTa",
+        "answer": [
+            "BERT, RoBERTa, ELECTRA, GPT-2, and XLM-RoBERTa."
+        ]
+    },
+    {
+        "output": "The restrictions on the use of Broadjam's servers are that they must be used for lawful purposes only and in accordance with this Agreement.",
+        "answer": [
+            "No excessive overloading and no use for illegal activity."
+        ]
+    },
+    {
+        "output": "The vacuum processing system is configured with a plurality of juxtaposed cassette stands and a carrier for carrying a wafer from or to the cassette stands. The vacuum processing apparatus comprises an air loader equipped with a plurality of juxtaposed",
+        "answer": [
+            "Multiple vacuum processing apparatuses are arranged in parallel."
+        ]
+    },
+    {
+        "output": "21",
+        "answer": [
+            "21."
+        ]
+    },
+    {
+        "output": "1.2 $\\mu\\_B$/Mn.",
+        "answer": [
+            "1425 $\\mu_{B}$."
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "Unknown."
+        ]
+    },
+    {
+        "output": "Never.",
+        "answer": [
+            "August 25."
+        ]
+    },
+    {
+        "output": "The benefits of using binary variables in the SLAS formulation are to reduce computational overload and aid numerical computations. They replace integer variables and help ensure feasibility tolerance and safety margins.",
+        "answer": [
+            "Reduced computational complexity."
+        ]
+    },
+    {
+        "output": "Users can go to the official website at <https://python.quectel.com/download> for troubleshooting and support.",
+        "answer": [
+            "Online documentation, QuecPython community, online support: QQ group 445121768."
+        ]
+    },
+    {
+        "output": "The symptoms of alpha thalassemia major include severe anemia, jaundice, enlarged spleen and liver, bone deformities, and an increased risk of infections.",
+        "answer": [
+            "Severe anemia that begins even before birth."
+        ]
+    },
+    {
+        "output": "24 October 2017.",
+        "answer": [
+            "October 2001."
+        ]
+    },
+    {
+        "output": "Smartphones are more compact and power constrained, while tablets are more like PCs both technically and economically.",
+        "answer": [
+            "Smartphones are more compact and power constrained."
+        ]
+    },
+    {
+        "output": "Spending cuts.",
+        "answer": [
+            "The sticking point in the political showdown over the budget is how much spending to cut."
+        ]
+    },
+    {
+        "output": "The Director is responsible for carrying out the functions assigned under the act.",
+        "answer": [
+            "The Director of Town and Country Planning is responsible for carrying out the functions assigned under the act."
+        ]
+    },
+    {
+        "output": "The framework captures the reduced-order dynamics by employing a propagator in the latent space that maps the high-dimensional state to a lower-dimensional representation.",
+        "answer": [
+            "By using a propagator in the latent space."
+        ]
+    },
+    {
+        "output": "Deploy nanobots.",
+        "answer": [
+            "Keep deploying and harvesting your bases to earn experience points and level up quickly."
+        ]
+    },
+    {
+        "output": "The electron correlation parameter, $\\Gamma\\_e$, is defined in equation \\ref{eqn:gamma\\_e}.",
+        "answer": [
+            "It is the ratio of the average unscreened electron-electron potential energy to kinetic energy."
+        ]
+    },
+    {
+        "output": "Players can skip dialogue on the quest map by pressing the 'SKIP' button.",
+        "answer": [
+            "Players can skip dialogue on the quest map by pressing the 'SKIP' button."
+        ]
+    },
+    {
+        "output": "52",
+        "answer": [
+            "Four years."
+        ]
+    },
+    {
+        "output": "None mentioned.",
+        "answer": [
+            "The IAF is considering the acquisition of the Airbus A330 MRTT (Multi-Role Tanker Transport) besides the Boeing C-17."
+        ]
+    },
+    {
+        "output": "The lenses may become permanently yellowed.",
+        "answer": [
+            "Exposure to sunlight or strong light may cause permanent yellow spot damage on the screen."
+        ]
+    },
+    {
+        "output": "To balance out the costs.",
+        "answer": [
+            "Because his roommate smokes."
+        ]
+    },
+    {
+        "output": "The London Directory proclaimed to contain “An ALPHABETICAL LIST OF THE NAMES and PLACES of ABODE of the MERCHANTS and PRINCIPAL TRADERS of the Cities of LONDON and WESTMINSTER, the",
+        "answer": [
+            "An alphabetical list of names and places of abode of the merchants and principal traders of the cities of London and Westminster, the Borough of Southwark, and their environs, with the number affixed to each house."
+        ]
+    },
+    {
+        "output": "It becomes less.",
+        "answer": [
+            "It becomes a bit less so that what's off to the left or right can be better noted."
+        ]
+    },
+    {
+        "output": "The group's request to the Connecticut DEEP Commissioner is to appoint a blue ribbon commission to conduct research and develop a management plan for Whalebone Cove.",
+        "answer": [
+            "Appointing a blue ribbon commission to conduct the research and develop the management plan and denying or defering approval on any applications for new docks in the Cove until the management plan can be developed and implemented."
+        ]
+    },
+    {
+        "output": "Power-law distribution.",
+        "answer": [
+            "Power-law functions."
+        ]
+    },
+    {
+        "output": "$f'\\left(x\\right)  = ",
+        "answer": [
+            "It depends on the value of x, either 0 or (6x^2cos(x^2)+sin(x^2))/(3(x^2)^(1/3))."
+        ]
+    },
+    {
+        "output": "The John F. Kennedy Profiles in Courage Award.",
+        "answer": [
+            "In 2009, Brooksley Born received the John F. Kennedy Profiles in Courage Award."
+        ]
+    },
+    {
+        "output": "The paper aims to solve nonlinear system vibration problems by computing transient responses of nonlinear oscillators under arbitrary irregular excitations based on a combination of a pole-residue operation and Volterra series. The proposed method is more efficient than traditional numerical methods and can provide meaningful physical and mathematical insights",
+        "answer": [
+            "The paper aims to solve nonlinear system vibration problems efficiently."
+        ]
+    },
+    {
+        "output": "Fuller's Ranch",
+        "answer": [
+            "The first white settlement in McPherson County was Fuller's Ranch, established by Charles O. Fuller."
+        ]
+    },
+    {
+        "output": "The normalized least mean square (NLMS) algorithm is engaged in the PLMS-PPIC method.",
+        "answer": [
+            "The normalized least mean square (NLMS) algorithm."
+        ]
+    },
+    {
+        "output": "It leads to overconfidence in the robot's prediction, making it more difficult to reach the correct goal compared to a method that ignores path preference.",
+        "answer": [
+            "The belief entropy decreases more steadily."
+        ]
+    },
+    {
+        "output": "The two ground states observed for indeno[1,2-a]fluorene on NaCl surfaces are an open-shell state and a closed-shell state.",
+        "answer": [
+            "Open-shell π-diradical state and closed-shell state with a para-quinodimethane moiety."
+        ]
+    },
+    {
+        "output": "The main focus of the research paper is to study the nuclear liquid-gas transition in the strong coupling regime of lattice QCD and determine the baryon mass and nuclear interactions as a function of the quark mass. The paper also discusses the limitations of the dual formulation and the sign problem in lattice QCD",
+        "answer": [
+            "Nuclear liquid-gas transition in lattice QCD."
+        ]
+    },
+    {
+        "output": "γ h",
+        "answer": [
+            "γh."
+        ]
+    },
+    {
+        "output": "William Rokebye.",
+        "answer": [
+            "Sir Richard."
+        ]
+    },
+    {
+        "output": "Thalassemias are classified based on the globin that is affected.",
+        "answer": [
+            "According to the globin that is affected (alpha or beta)."
+        ]
+    },
+    {
+        "output": "McPherson",
+        "answer": [
+            "McPherson."
+        ]
+    },
+    {
+        "output": "1867",
+        "answer": [
+            "McPherson County was established as a county in 1867."
+        ]
+    },
+    {
+        "output": "1. 下载Ganache安装包并解压。",
+        "answer": [
+            "使用安装包安装Ganache；打开程序，用户可以从显示的界面中查看账户、区块、交易和日志；点击“设置”，用户可以设置绑定的ip和端口、账户数量以及gas限制等，点击“restart”后设置生效。此时，Ganache已经在本机运行了一个以太坊私有网络，并绑定了8545端口。."
+        ]
+    },
+    {
+        "output": "Decorrelating the data before running the PLS algorithm helps to remove the dependence in the data and results in more accurate regression coefficient estimators. The decorrelated data leads to a better prediction on the test set. The Pearson correlation between the true channel diameter on the test set and the prediction based on decorrelated",
+        "answer": [
+            "Decorrelating the data before running the PLS algorithm improves the performance of the algorithm."
+        ]
+    },
+    {
+        "output": "C-GDBN",
+        "answer": [
+            "The generative interactive model used in the method is called the Coupled Generalized Dynamic Bayesian Network (C-GDBN)."
+        ]
+    },
+    {
+        "output": "Spin injection into non-magnetic semiconductors, electrical manipulation of carrier-induced magnetism in magnetic semiconductors, nanodevices such as memory nanodots or nanochannels for spin injection.",
+        "answer": [
+            "Spin injection into non magnetic semiconductors, or electrical manipulation of carrier induced magnetism in magnetic semiconductors."
+        ]
+    },
+    {
+        "output": "62",
+        "answer": [
+            "62."
+        ]
+    },
+    {
+        "output": "1766",
+        "answer": [
+            "1766."
+        ]
+    },
+    {
+        "output": "The main topic of the text is the Iraq War.",
+        "answer": [
+            "The main topic of the text is Iraq's politics and current situation."
+        ]
+    },
+    {
+        "output": "SNNs may serve as a shortcut to explore the biological visual cortex and provide new insights into the visual processing mechanisms of macaque and mouse. They outperform their counterparts of CNNs and exhibit similar properties as CNNs in the further analyses. However, the study of task-",
+        "answer": [
+            "SNNs have the potential to better model and explain the functional hierarchy and mechanisms of the visual system."
+        ]
+    },
+    {
+        "output": "Deputy Prime Minister and Minister of Finance.",
+        "answer": [
+            "He became deputy prime minister and minister of finance."
+        ]
+    },
+    {
+        "output": "85.61%",
+        "answer": [
+            "85.61%."
+        ]
+    },
+    {
+        "output": "The smaller the specific-heat ratio, the slower the average motion of the bubble.",
+        "answer": [
+            "The specific-heat ratio affects the average motion of the bubble. The bubbles with smaller specific-heat ratios have slower average motion."
+        ]
+    },
+    {
+        "output": "McPherson County is located in the U.S. state of Kansas.",
+        "answer": [
+            "McPherson County is located in the U.S. state of Kansas."
+        ]
+    },
+    {
+        "output": "$O(t, L_{\\parallel}; S_\\Delta) = L_{\\parallel}^{-\\beta/[\\nu(1+\\Delta)]} \\tilde f\\_O(t/L_{\\parallel}^{z/(1+\\Delta)}; S_\\Delta)$",
+        "answer": [
+            "O(t, L_{\\parallel}; S_\\Delta) = L_{\\parallel}^{-\\beta/[\\nu(1+\\Delta)]} \\tilde f_O(t/L_{\\parallel}^{z/(1+\\Delta)}; S_\\Delta)."
+        ]
+    },
+    {
+        "output": "The relationships between catch per set and fishing behavior variables are different for different measures of catch per unit effort (CPUE).",
+        "answer": [
+            "The relationships between catch per set and fishing behavior variables differ when comparing unstandardized CPUE and standardized CPUE."
+        ]
+    },
+    {
+        "output": "Romance",
+        "answer": [
+            "Romance novels and women's fiction."
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "No."
+        ]
+    },
+    {
+        "output": "URPC2017 and URPC2018 are the datasets used in this community for research.",
+        "answer": [
+            "URPC2017, URPC2018, URPC2019, URPC2020_ZJ and URPC2020_DL."
+        ]
+    },
+    {
+        "output": "The research opportunity mentioned is a study on the effectiveness of different therapy approaches for individuals with Asperger's Syndrome.",
+        "answer": [
+            "A study on the effects of Brazilian Jiu Jitsu and psychotherapy on people with autism."
+        ]
+    }
+]

data/results/longbench/test/generation/musique.json

@@ -0,0 +1,1276 @@
+[
+    {
+        "output": "Salma Hayek.",
+        "answer": [
+            "Maria Bello"
+        ]
+    },
+    {
+        "output": "James Chadwick.",
+        "answer": [
+            "Manhattan Project"
+        ]
+    },
+    {
+        "output": "The given passage does not mention Callum McManaman's birthplace.",
+        "answer": [
+            "Knowsley",
+            "Metropolitan Borough of Knowsley"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Sebastian Cabot"
+        ]
+    },
+    {
+        "output": "The spouse of the actor who played Hannibal Smith in The A-Team is Joy Harmon.",
+        "answer": [
+            "Sherry Boucher"
+        ]
+    },
+    {
+        "output": "The last time Peter Till's sports team beat the winner of the 1894-95 FA Cup was in the 2011",
+        "answer": [
+            "1 December 2010"
+        ]
+    },
+    {
+        "output": "The child of the cast member of Green Ice is not mentioned in the given passages.",
+        "answer": [
+            "Tatum O'Neal"
+        ]
+    },
+    {
+        "output": "The passage does not mention the birthplace of La Silvia's composer.",
+        "answer": [
+            "22"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific date for when military instruction started at Larry Alcala's educational institution.",
+        "answer": [
+            "1912"
+        ]
+    },
+    {
+        "output": "ATS-6",
+        "answer": [
+            "ATS - 6 (Applications Technology Satellite - 6)",
+            "ATS-6"
+        ]
+    },
+    {
+        "output": "Robert Khayat.",
+        "answer": [
+            "James Howard Meredith",
+            "James Meredith"
+        ]
+    },
+    {
+        "output": "The Hickinbottom Award is given out by the Royal Society of Chemistry.",
+        "answer": [
+            "University of Cambridge",
+            "Cambridge"
+        ]
+    },
+    {
+        "output": "Tom Hood.",
+        "answer": [
+            "Tom Hood",
+            "Thomas Hood"
+        ]
+    },
+    {
+        "output": "The spouse of the person who voices Jarvis in Iron Man is Bethany Price.",
+        "answer": [
+            "Jennifer Connelly"
+        ]
+    },
+    {
+        "output": "The source of the river that is the mouth of the Caledon River is the Caledon River.",
+        "answer": [
+            "Thaba Putsoa"
+        ]
+    },
+    {
+        "output": "The Goodpasture Bridge.",
+        "answer": [
+            "Rialto Bridge",
+            "Ponte di Rialto"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "January 2015"
+        ]
+    },
+    {
+        "output": "The producer of Julius Caesar studied and worked in the United States.",
+        "answer": [
+            "Clifton College"
+        ]
+    },
+    {
+        "output": "Ankara",
+        "answer": [
+            "Malko Tarnovo Municipality"
+        ]
+    },
+    {
+        "output": "The song \"Heartbeat\" is named after the band's drummer, Natalie Albino.",
+        "answer": [
+            "Natalie Albino",
+            "Nina Sky"
+        ]
+    },
+    {
+        "output": "University of Oxford",
+        "answer": [
+            "University of Toronto",
+            "UT"
+        ]
+    },
+    {
+        "output": "The publisher of Labyrinth ended in 1984.",
+        "answer": [
+            "1986"
+        ]
+    },
+    {
+        "output": "FDA",
+        "answer": [
+            "Food Safety Modernization Act (FSMA)"
+        ]
+    },
+    {
+        "output": "The president of the newly declared independent country is José Ramos-Horta.",
+        "answer": [
+            "Francisco Guterres"
+        ]
+    },
+    {
+        "output": "The British general in the Battle of New Orleans was Major General Sir Edward Pakenham.",
+        "answer": [
+            "General Edward Pakenham",
+            "Edward Pakenham"
+        ]
+    },
+    {
+        "output": "The passage does not mention Sean Hampton's birthplace.",
+        "answer": [
+            "in Northern Florida",
+            "Northern Florida"
+        ]
+    },
+    {
+        "output": "The World Taekwondo Federation.",
+        "answer": [
+            "Thomas Bach"
+        ]
+    },
+    {
+        "output": "The war in which The Things They Carried is set is the Vietnam War. Conscription was introduced in Australia during World War I. Grievous Bodily",
+        "answer": [
+            "1964"
+        ]
+    },
+    {
+        "output": "The answer is Susilo Bambang Yudhoyono.",
+        "answer": [
+            "Francisco Guterres"
+        ]
+    },
+    {
+        "output": "Prince Philip, Duke of Edinburgh.",
+        "answer": [
+            "Philip Mountbatten"
+        ]
+    },
+    {
+        "output": "1145",
+        "answer": [
+            "1065"
+        ]
+    },
+    {
+        "output": "Shell announced it was in the process of selling its 269 unconventional shale gas wells in the United States.",
+        "answer": [
+            "trying to find a potential buyer for all of its operations in Finland",
+            "FIN",
+            "fi",
+            "Finland"
+        ]
+    },
+    {
+        "output": "Abraham married Keturah after the death of Sarah.",
+        "answer": [
+            "Keturah"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific location for John Phan's birthplace.",
+        "answer": [
+            "South Central Coast"
+        ]
+    },
+    {
+        "output": "Michael Bublé",
+        "answer": [
+            "Michael Bublé"
+        ]
+    },
+    {
+        "output": "The Live Life Loud album's band signed to the SteepleChase label.",
+        "answer": [
+            "Fair Trade Services"
+        ]
+    },
+    {
+        "output": "Christina Gyllenstierna.",
+        "answer": [
+            "Svante Stensson Sture"
+        ]
+    },
+    {
+        "output": "The record label of the Metalworks Studio founder is Metalworks Records.",
+        "answer": [
+            "TML Entertainment"
+        ]
+    },
+    {
+        "output": "The president of the new country is Joko Widodo.",
+        "answer": [
+            "Francisco Guterres"
+        ]
+    },
+    {
+        "output": "The passage does not mention the city where Flora's painter died.",
+        "answer": [
+            "22"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Green Bay"
+        ]
+    },
+    {
+        "output": "The highest city in the state where Dell ranks sixth by revenue is not mentioned in the given passages. The passages mention Fort Davis, Texas,",
+        "answer": [
+            "Fort Davis"
+        ]
+    },
+    {
+        "output": "Jennifer Parker was played by Claudia Wells in the first film and Elisabeth Shue in the second and third film.",
+        "answer": [
+            "Claudia Wells"
+        ]
+    },
+    {
+        "output": "The quote \"by the skin of your teeth\" comes from the Bible, specifically from Job 19:20 in the King James Version.",
+        "answer": [
+            "Genesis 3:15"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Green Bay"
+        ]
+    },
+    {
+        "output": "The passage does not mention the number of times plague occurred in the place where Titian died. It only mentions that the painting was sold to the American collect",
+        "answer": [
+            "22"
+        ]
+    },
+    {
+        "output": "The source of the river is not mentioned in the given passages.",
+        "answer": [
+            "Thaba Putsoa"
+        ]
+    },
+    {
+        "output": "Common Sense was an important work because it catalyzed the call for independence from Great Britain and was widely read by American Patriots. It helped inspire",
+        "answer": [
+            "crystallized the rebellious demand for independence from Great Britain",
+            "Great Britain"
+        ]
+    },
+    {
+        "output": "Poplarville, Mississippi.",
+        "answer": [
+            "Pearl River County",
+            "Pearl River County, Mississippi"
+        ]
+    },
+    {
+        "output": "The person who acted in the comedy film Crime School won his only Oscar for the film The Caine Mutiny.",
+        "answer": [
+            "The African Queen"
+        ]
+    },
+    {
+        "output": "The Caine Mutiny.",
+        "answer": [
+            "The African Queen"
+        ]
+    },
+    {
+        "output": "Manchester City.",
+        "answer": [
+            "Egypt national football team"
+        ]
+    },
+    {
+        "output": "The province of Gonbad-e Qabus County is located in Iran.",
+        "answer": [
+            "in the north-east of the country south of the Caspian Sea",
+            "Caspian Sea"
+        ]
+    },
+    {
+        "output": "The song \"Turn Me On\" is performed by Norah Jones. The song \"Feelin' the Same Way\" was written by Lee Alexander and produced by",
+        "answer": [
+            "John D. Loudermilk"
+        ]
+    },
+    {
+        "output": "The actor who played the voice of John Smith in Pocahontas did not attend any institute. The voice was provided by actor Mel Gibson.",
+        "answer": [
+            "National Institute of Dramatic Art",
+            "NIDA"
+        ]
+    },
+    {
+        "output": "Khalid ibn Walid.",
+        "answer": [
+            "Abu Ubaidah"
+        ]
+    },
+    {
+        "output": "Walker Pond",
+        "answer": [
+            "River Thames"
+        ]
+    },
+    {
+        "output": "The Democratic Party.",
+        "answer": [
+            "January 2015"
+        ]
+    },
+    {
+        "output": "The Nothing Suits Me Like a Suit artist plays the character of the Joker in Batman Under the Red Hood.",
+        "answer": [
+            "Nightwing / Dick Grayson",
+            "Nightwing",
+            "Dick Grayson",
+            "Batman",
+            "Robin"
+        ]
+    },
+    {
+        "output": "The death penalty was abolished in the country near the country where the writer of The Book Thief is a citizen of in 1975.",
+        "answer": [
+            "1989"
+        ]
+    },
+    {
+        "output": "Baranya County.",
+        "answer": [
+            "Green Bay"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Matthew Lawrence"
+        ]
+    },
+    {
+        "output": "Joey Lawrence.",
+        "answer": [
+            "Matthew Lawrence"
+        ]
+    },
+    {
+        "output": "The last time the team that Arthur James was a member of beat the 1894-95 FA cup winner was in the 200",
+        "answer": [
+            "1 December 2010"
+        ]
+    },
+    {
+        "output": "Jonas Öberg",
+        "answer": [
+            "Richard Stallman",
+            "rms"
+        ]
+    },
+    {
+        "output": "The God of the underworld in ancient Egypt is a part of the Osiris-Horus pantheon.",
+        "answer": [
+            "ancient Egyptian religion"
+        ]
+    },
+    {
+        "output": "The president born on the fourth of July was born in New York.",
+        "answer": [
+            "Plymouth Notch"
+        ]
+    },
+    {
+        "output": "Jennifer Parker.",
+        "answer": [
+            "Claudia Wells"
+        ]
+    },
+    {
+        "output": "Sire Records",
+        "answer": [
+            "Kanine Records"
+        ]
+    },
+    {
+        "output": "Mach's principle was named after Ernst Mach.",
+        "answer": [
+            "Charles University"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "average lifespan at 9.4 years"
+        ]
+    },
+    {
+        "output": "The president of the newly declared independent country is not mentioned in the given passages. The passages mention the Sambas Regency and the Commission of Tr",
+        "answer": [
+            "Francisco Guterres"
+        ]
+    },
+    {
+        "output": "The capital of the country is Georgetown.",
+        "answer": [
+            "Aden"
+        ]
+    },
+    {
+        "output": "Football",
+        "answer": [
+            "sports league",
+            "Sports league",
+            "league"
+        ]
+    },
+    {
+        "output": "Clan Lindsay",
+        "answer": [
+            "Scottish clan"
+        ]
+    },
+    {
+        "output": "Fleur-de-lis.",
+        "answer": [
+            "fleur - de-lis",
+            "Fleur-de-lis",
+            "fleur-de-lis"
+        ]
+    },
+    {
+        "output": "Greek",
+        "answer": [
+            "Greek mythology"
+        ]
+    },
+    {
+        "output": "The oldest college in the state that contains Borough House Plantation is The Citadel.",
+        "answer": [
+            "College of Charles Towne",
+            "Charles Towne"
+        ]
+    },
+    {
+        "output": "The answer is Cleveland County, North Carolina.",
+        "answer": [
+            "Cabarrus County"
+        ]
+    },
+    {
+        "output": "Port Angeles, Washington.",
+        "answer": [
+            "Clatskanie",
+            "Clatskanie, Oregon"
+        ]
+    },
+    {
+        "output": "James Chadwick discovered that the number of protons in each element's atoms is unique.",
+        "answer": [
+            "Manhattan Project"
+        ]
+    },
+    {
+        "output": "The story of the character from The Bourne Deception was based on the character of Don Pedro from William Shakespeare's play Much Ado About Nothing.",
+        "answer": [
+            "the novel of the same name by Robert Ludlum",
+            "Robert Ludlum"
+        ]
+    },
+    {
+        "output": "Wealth and net worth.",
+        "answer": [
+            "ease of use and enhanced support for Plug and Play"
+        ]
+    },
+    {
+        "output": "The Spinning Image.",
+        "answer": [
+            "Aaron Benward"
+        ]
+    },
+    {
+        "output": "The passage does not mention the place of birth of Bajazet's composer.",
+        "answer": [
+            "22"
+        ]
+    },
+    {
+        "output": "The company that published Starship Command was Acornsoft. It was formed in 1982 and ended in 1998.",
+        "answer": [
+            "1986"
+        ]
+    },
+    {
+        "output": "Henrietta Cecilia Smit.",
+        "answer": [
+            "Johanne Luise Heiberg"
+        ]
+    },
+    {
+        "output": "The character played by the actress who played Thelma in the movie Thelma and Louise is Dottie Henson.",
+        "answer": [
+            "Dottie Hinson"
+        ]
+    },
+    {
+        "output": "NES",
+        "answer": [
+            "NES",
+            "Family Computer",
+            "Famicom",
+            "Nintendo Entertainment System"
+        ]
+    },
+    {
+        "output": "The passage does not mention Alan O'Neil or any sports team he was a member of. The passage mentions the 1894-95",
+        "answer": [
+            "1 December 2010"
+        ]
+    },
+    {
+        "output": "The spouse of the person who plays Batman in the Lego Batman Movie is not mentioned in the given passages.",
+        "answer": [
+            "Amy Poehler"
+        ]
+    },
+    {
+        "output": "The passage does not mention the specific reduction in population of the region where Baton a feu originated due to the Black Death.",
+        "answer": [
+            "30% to 65%"
+        ]
+    },
+    {
+        "output": "The passage does not provide a clear answer to the question.",
+        "answer": [
+            "August 3, 1769"
+        ]
+    },
+    {
+        "output": "The name of the castle is not mentioned in the given passages.",
+        "answer": [
+            "Casa Loma"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Min Zhou",
+            "Carl L. Bankston"
+        ]
+    },
+    {
+        "output": "Dennis Quaid.",
+        "answer": [
+            "Meg Ryan"
+        ]
+    },
+    {
+        "output": "Conservative Party of Canada",
+        "answer": [
+            "Andrew Scheer"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific answer to this question.",
+        "answer": [
+            "24:00"
+        ]
+    },
+    {
+        "output": "The place of birth of the performer of Mother-in-Law elected its first black Mayor in 1962.",
+        "answer": [
+            "1970s"
+        ]
+    },
+    {
+        "output": "The passage does not mention Ken Faulkner or his team beating the 1894-95 FA Cup winner.",
+        "answer": [
+            "1 December 2010"
+        ]
+    },
+    {
+        "output": "2005",
+        "answer": [
+            "2005"
+        ]
+    },
+    {
+        "output": "The mascot of the school that owns Goss Stadium at Coleman Field is the Beavers.",
+        "answer": [
+            "Benny Beaver"
+        ]
+    },
+    {
+        "output": "Jennifer Connelly.",
+        "answer": [
+            "Jennifer Connelly"
+        ]
+    },
+    {
+        "output": "Scarlett O'Hara's spouse is Rhett Butler, played by Clark Gable.",
+        "answer": [
+            "Laurence Olivier"
+        ]
+    },
+    {
+        "output": "Lucy Mack Smith.",
+        "answer": [
+            "Lucy Mack Smith"
+        ]
+    },
+    {
+        "output": "The question does not provide enough information to determine the current mayor.",
+        "answer": [
+            "Lance Bottoms"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Cabarrus County"
+        ]
+    },
+    {
+        "output": "SATCOM-1",
+        "answer": [
+            "ATS - 6 (Applications Technology Satellite - 6)",
+            "ATS-6"
+        ]
+    },
+    {
+        "output": "The river on which the Lostock Dam is located is the Cooks River.",
+        "answer": [
+            "Hunter River"
+        ]
+    },
+    {
+        "output": "The population of state A is not mentioned in the given passages.",
+        "answer": [
+            "1,335,907"
+        ]
+    },
+    {
+        "output": "The capital of the county that shares a border with the county where KRSU is licensed to broadcast is St. Paul.",
+        "answer": [
+            "Green Bay"
+        ]
+    },
+    {
+        "output": "Indonesia",
+        "answer": [
+            "Francisco Guterres"
+        ]
+    },
+    {
+        "output": "The male star of the 1921 film Money is Rudolph Valentino.",
+        "answer": [
+            "Anthony Ainley"
+        ]
+    },
+    {
+        "output": "The symbol of the Saints from the headquarters location of Ten High's manufacturer is the fleur-de-lis.",
+        "answer": [
+            "fleur - de-lis",
+            "Fleur-de-lis",
+            "fleur-de-lis"
+        ]
+    },
+    {
+        "output": "Tanzania.",
+        "answer": [
+            "Tanzania"
+        ]
+    },
+    {
+        "output": "Johnny Cash",
+        "answer": [
+            "Michael Bublé"
+        ]
+    },
+    {
+        "output": "Conservative Party of Canada",
+        "answer": [
+            "Andrew Scheer"
+        ]
+    },
+    {
+        "output": "Cabo Verde",
+        "answer": [
+            "central Atlantic Ocean",
+            "Atlantic Ocean",
+            "Atlantic"
+        ]
+    },
+    {
+        "output": "Ben Affleck.",
+        "answer": [
+            "Fred O'Bannion"
+        ]
+    },
+    {
+        "output": "Neil Patrick Harris plays the character of the Joker in Batman Under the Red Hood.",
+        "answer": [
+            "Nightwing / Dick Grayson",
+            "Nightwing",
+            "Dick Grayson",
+            "Batman",
+            "Robin"
+        ]
+    },
+    {
+        "output": "Audrey Hepburn won a Tony Award for her performance in the play Ondine.",
+        "answer": [
+            "for Best Performance by a Leading Actress in a Play in Ondine",
+            "leading role",
+            "star",
+            "leading actress",
+            "lead"
+        ]
+    },
+    {
+        "output": "Serbia.",
+        "answer": [
+            "Pristina"
+        ]
+    },
+    {
+        "output": "Orange River",
+        "answer": [
+            "Thaba Putsoa"
+        ]
+    },
+    {
+        "output": "The answer is not mentioned in the given passages.",
+        "answer": [
+            "Claudia Wells"
+        ]
+    },
+    {
+        "output": "Mrs. Puff.",
+        "answer": [
+            "Mr. Lawrence",
+            "Doug Lawrence"
+        ]
+    },
+    {
+        "output": "The war with Pyrrhus occurred between 323 and 272 BCE.",
+        "answer": [
+            "323–272 BC"
+        ]
+    },
+    {
+        "output": "Closing and liquidating all of its stores.",
+        "answer": [
+            "trying to find a potential buyer for all of its operations in Finland",
+            "FIN",
+            "fi",
+            "Finland"
+        ]
+    },
+    {
+        "output": "The Battle at Choo Hoey's birthplace ended on May 5, 1862.",
+        "answer": [
+            "15 February 1942"
+        ]
+    },
+    {
+        "output": "The president of the newly declared independent country is José Ramos-Horta. Sumardi was born in Indonesia.",
+        "answer": [
+            "Francisco Guterres"
+        ]
+    },
+    {
+        "output": "The song The Story sung by the performer of Give Up the Ghost was written by Brandi Carlile.",
+        "answer": [
+            "Phil Hanseroth"
+        ]
+    },
+    {
+        "output": "Thailand",
+        "answer": [
+            "Ko Phi Phi Leh",
+            "Ko Phi Phi Le"
+        ]
+    },
+    {
+        "output": "The author of Growing Up X is Ilyasah Shabazz.",
+        "answer": [
+            "Min Zhou",
+            "Carl L. Bankston"
+        ]
+    },
+    {
+        "output": "The answer is Horry County, South Carolina.",
+        "answer": [
+            "Kenton County",
+            "Kenton County, Kentucky"
+        ]
+    },
+    {
+        "output": "The Oscar winner for Best Actor in 2006 also received a Golden Globe for Best Actor in a Motion Picture Drama.",
+        "answer": [
+            "nominated for an Academy Award for Best Supporting Actor",
+            "Academy Award for Best Supporting Actor"
+        ]
+    },
+    {
+        "output": "The given passages do not mention the Smile performer's birthday.",
+        "answer": [
+            "1 January 1986"
+        ]
+    },
+    {
+        "output": "The performer of Emotional Rain is on the Impulse! label.",
+        "answer": [
+            "Attic Records",
+            "Attic"
+        ]
+    },
+    {
+        "output": "The majority party in the House of Representatives gained control of the body which approves members of the Cabinet in 1911.",
+        "answer": [
+            "January 2015"
+        ]
+    },
+    {
+        "output": "The Kentucky Tavern headquarters location saints symbol is called the Fleur-de-lis.",
+        "answer": [
+            "fleur - de-lis",
+            "Fleur-de-lis",
+            "fleur-de-lis"
+        ]
+    },
+    {
+        "output": "Wenzhou, Zhejiang.",
+        "answer": [
+            "Yongjia County"
+        ]
+    },
+    {
+        "output": "Luke Bryan sings Home Alone Tonight with Karen Fairchild of Little Big Town.",
+        "answer": [
+            "Karen Fairchild"
+        ]
+    },
+    {
+        "output": "Katzenstein Castle",
+        "answer": [
+            "Casa Loma"
+        ]
+    },
+    {
+        "output": "The answer is Maureen O'Hara.",
+        "answer": [
+            "Lana Wood"
+        ]
+    },
+    {
+        "output": "Rome.",
+        "answer": [
+            "La Goulette",
+            "Tunis",
+            "Rome",
+            "Roma"
+        ]
+    },
+    {
+        "output": "The Occupation of Gori occurred in the northern part of Erich Zakowski's birthplace in the now Russian area.",
+        "answer": [
+            "names were replaced by new Russian names"
+        ]
+    },
+    {
+        "output": "Charles County, Maryland.",
+        "answer": [
+            "Charles County",
+            "Charles County, Maryland"
+        ]
+    },
+    {
+        "output": "The party that controls the House of Representatives takes control of the branch that determines the rules of the US House of Representatives. The rules of the US Senate are",
+        "answer": [
+            "January 2015"
+        ]
+    },
+    {
+        "output": "The sports team that the all-time highest goal scorer in the Spain national team achieved a series of wins in 2009 is not mentioned in",
+        "answer": [
+            "continental treble"
+        ]
+    },
+    {
+        "output": "Salma Hayek plays the wife of the actor who produced The Chosen One.",
+        "answer": [
+            "Joyce Van Patten"
+        ]
+    },
+    {
+        "output": "Bobby Brown.",
+        "answer": [
+            "a Western District uniformed officer"
+        ]
+    },
+    {
+        "output": "The passage does not mention a merger between Burroughs Corporation and another corporation. It only mentions that Unisys Corporation was formed in 1",
+        "answer": [
+            "1986"
+        ]
+    },
+    {
+        "output": "The country whose co-official language was used by the director of Group Portrait with a Lady first attended the Olympics as an independent team in 19",
+        "answer": [
+            "2016"
+        ]
+    },
+    {
+        "output": "Sébastien Fauqué.",
+        "answer": [
+            "Menachem Mendel Schneersohn"
+        ]
+    },
+    {
+        "output": "The answer is not given in the passages.",
+        "answer": [
+            "Michael Bublé"
+        ]
+    },
+    {
+        "output": "The head of the developer of Simon is Sam Simon.",
+        "answer": [
+            "ADM Michael S. Rogers",
+            "Michael S. Rogers"
+        ]
+    },
+    {
+        "output": "The last time the team that Terry Twell was a member of beat the 194-95 winner of the FA Cup was in the 1",
+        "answer": [
+            "1 December 2010"
+        ]
+    },
+    {
+        "output": "The passage does not mention an artist who painted Head I. The passage mentions Ivanoff Head, a small rocky headland or probable island located in Ant",
+        "answer": [
+            "Nicholas Bacon",
+            "Sir Nicholas Bacon"
+        ]
+    },
+    {
+        "output": "The origin of the river Augrabies Falls is in the Drakensberg Mountains in South Africa.",
+        "answer": [
+            "Thaba Putsoa"
+        ]
+    },
+    {
+        "output": "The BBC Footballer of the Year is a member of the San Marino national team.",
+        "answer": [
+            "Egypt national football team"
+        ]
+    },
+    {
+        "output": "France",
+        "answer": [
+            "La Goulette"
+        ]
+    },
+    {
+        "output": "The passage does not specify the number of people in British colonies where London broil cut comes from.",
+        "answer": [
+            "2 million"
+        ]
+    },
+    {
+        "output": "The spouse of the person who voices Jarvis in the Avengers Age of Ultron is Linda Cardellini.",
+        "answer": [
+            "Jennifer Connelly"
+        ]
+    },
+    {
+        "output": "DeKalb County, Georgia.",
+        "answer": [
+            "Presque Isle County"
+        ]
+    },
+    {
+        "output": "The rapper Jay-Z was inspired by three different relationships when writing Song Cry.",
+        "answer": [
+            "three different relationships he had in the past"
+        ]
+    },
+    {
+        "output": "The administrative territorial entity that includes the place that KTRP is licensed to broadcast to is the United States.",
+        "answer": [
+            "Canyon County",
+            "Canyon County, Idaho"
+        ]
+    },
+    {
+        "output": "Tom Hanks played the husband of Geena Davis who played Thelma in Thelma and Louise.",
+        "answer": [
+            "Bill Pullman"
+        ]
+    },
+    {
+        "output": "Indonesia",
+        "answer": [
+            "Dutch East Indies"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "Cabo Delgado Province"
+        ]
+    },
+    {
+        "output": "Theo van Doesburg",
+        "answer": [
+            "Association for Computing Machinery",
+            "ACM"
+        ]
+    },
+    {
+        "output": "Clear Springs is a historic settlement in Guadalupe County, Texas.",
+        "answer": [
+            "Bell County"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "Tamaulipas"
+        ]
+    },
+    {
+        "output": "The answer is not given in the given passages.",
+        "answer": [
+            "Sebastian Cabot"
+        ]
+    },
+    {
+        "output": "Colin Firth played the monarch at the end of WW2 in The King's Speech.",
+        "answer": [
+            "Colin Firth"
+        ]
+    },
+    {
+        "output": "Stan's voice is performed by actor and comedian Trey Parker.",
+        "answer": [
+            "Denver",
+            "Denver, Colorado"
+        ]
+    },
+    {
+        "output": "Thailand",
+        "answer": [
+            "Ko Phi Phi Leh",
+            "Ko Phi Phi Le"
+        ]
+    },
+    {
+        "output": "The competition that the 1965 Federation Cup is part of is named after Billie Jean King.",
+        "answer": [
+            "International Tennis Federation",
+            "ITF"
+        ]
+    },
+    {
+        "output": "The president of the newly declared independent country is José Ramos-Horta. The Commission of Truth and Friendship was established between Timor-L",
+        "answer": [
+            "Francisco Guterres"
+        ]
+    },
+    {
+        "output": "Colin Firth played the King of England in 1950.",
+        "answer": [
+            "Colin Firth"
+        ]
+    },
+    {
+        "output": "The River Ekulu is a body of water in the city of Enugu in Enugu State, southeastern Nigeria. It is the largest",
+        "answer": [
+            "River Thames"
+        ]
+    },
+    {
+        "output": "The author of The Happy Journey to Trenton and Camden, Thornton Wilder, won a Tony Award and a Drama Desk",
+        "answer": [
+            "Pulitzer Prize for Drama"
+        ]
+    },
+    {
+        "output": "The designer of the Lap Engine was educated at the University of Glasgow.",
+        "answer": [
+            "University of Glasgow"
+        ]
+    },
+    {
+        "output": "The father of the Labo M performer is Leo van der Laan.",
+        "answer": [
+            "Louis Chedid"
+        ]
+    },
+    {
+        "output": "The Gozzi Altarpiece was made by Fra Angelico, who was a Dominican friar. Roncalli was a Dominican priest and",
+        "answer": [
+            "for the conclave in Rome",
+            "Rome",
+            "Roma"
+        ]
+    },
+    {
+        "output": "The Adam Smith Prize is awarded by the University of Cambridge for the best overall examination performance and best dissertation in Part IIB of the Economics Tri",
+        "answer": [
+            "University of Cambridge",
+            "Cambridge"
+        ]
+    },
+    {
+        "output": "The given passage does not mention Germany invading any country. Therefore, there is no answer to the question.",
+        "answer": [
+            "Lech Wałęsa"
+        ]
+    },
+    {
+        "output": "Coolsingel",
+        "answer": [
+            "Het Scheur"
+        ]
+    },
+    {
+        "output": "Blaine Larsen sings Home Alone Tonight with the singer of \"I Don't Want This Night to End\".",
+        "answer": [
+            "Karen Fairchild"
+        ]
+    },
+    {
+        "output": "The school where Lawrence Landweber was educated is Brooklyn College.",
+        "answer": [
+            "City University of New York",
+            "The City University of New York"
+        ]
+    },
+    {
+        "output": "The capital of the county that shares a border with the county where WAPL is licensed to broadcast is Appleton.",
+        "answer": [
+            "Green Bay"
+        ]
+    },
+    {
+        "output": "The passage does not mention the first black student admitted to where Robert Khayat was educated.",
+        "answer": [
+            "James Howard Meredith",
+            "James Meredith"
+        ]
+    },
+    {
+        "output": "Lacey Chabert.",
+        "answer": [
+            "Lacey Chabert"
+        ]
+    },
+    {
+        "output": "The record label of the co-writer and recording artist of Permission to Fly is Touch.",
+        "answer": [
+            "Hollywood Records"
+        ]
+    },
+    {
+        "output": "The performer who released All Your Faded Things is on the Impulse! label.",
+        "answer": [
+            "Kill Rock Stars"
+        ]
+    },
+    {
+        "output": "The author of Miss Sara Sampson is Gotthold Ephraim Lessing.",
+        "answer": [
+            "Emilia Galotti"
+        ]
+    },
+    {
+        "output": "The majority party in the body which determines rules of the US House and US Senate gained control of the House in the 114th Congress which started",
+        "answer": [
+            "the 2010 election"
+        ]
+    },
+    {
+        "output": "The passage does not provide a specific year for Margaret Knox's spouse's passing.",
+        "answer": [
+            "1572"
+        ]
+    },
+    {
+        "output": "Rostov",
+        "answer": [
+            "Yaroslavl Oblast"
+        ]
+    },
+    {
+        "output": "The answer is the Qatar Stars League.",
+        "answer": [
+            "Qatar Stars League"
+        ]
+    },
+    {
+        "output": "The Heptarchy was part of the kingdom of Mercia, which was abolished in 924 AD.",
+        "answer": [
+            "918"
+        ]
+    },
+    {
+        "output": "The given passages do not provide information on the average salary of a working person with the same nationality as the producer of The Wild Women of Chast",
+        "answer": [
+            "$59,039"
+        ]
+    },
+    {
+        "output": "Campbell.",
+        "answer": [
+            "Santa Clara County",
+            "Santa Clara County, California"
+        ]
+    }
+]

data/results/longbench/test/generation/narrativeqa.json

@@ -0,0 +1,1202 @@
+[
+    {
+        "output": "Saltram is living with the Mulvilles.",
+        "answer": [
+            "He is a guest in the home of the Mulvilles."
+        ]
+    },
+    {
+        "output": "Ann does not return Mary's feelings of affection because she is already married.",
+        "answer": [
+            "Because she is in unrequited love with someone else"
+        ]
+    },
+    {
+        "output": "The witch lives on Atlas' mountain within a cavern, by a secret fountain.",
+        "answer": [
+            "The Atlas Mountains"
+        ]
+    },
+    {
+        "output": "To propose an escape plan.",
+        "answer": [
+            "To smuggle Socrates out of prison and into a life of exile."
+        ]
+    },
+    {
+        "output": "The story doesn't provide a clear answer. It only states that the Baron was angry and wanted to punish Otto. It doesn't explain why he didn't just kill him. The story doesn't provide enough context to understand the Baron's motivations.",
+        "answer": [
+            "Otto was so young."
+        ]
+    },
+    {
+        "output": "The doctor examined the body after the vicar had made the discovery of the dead woman. The doctor had been sent for and arrived after the vicar had made the discovery. The doctor did not provide a specific time for when the woman had been dead, but it was clear that the body had been there for some time. The doctor's examination of the body was not the focus of the story, so the exact length of time the woman had been dead was not given in detail. The story mainly focused on the investigation and the events leading up to the discovery of the body.",
+        "answer": [
+            "Six hours."
+        ]
+    },
+    {
+        "output": "The manager has an affair with the entertainer named Jof.",
+        "answer": [
+            "Lisa, the blacksmith's wife."
+        ]
+    },
+    {
+        "output": "Oscar.",
+        "answer": [
+            "Dr. Janosz Poha"
+        ]
+    },
+    {
+        "output": "American.",
+        "answer": [
+            "She is an American."
+        ]
+    },
+    {
+        "output": "The Witch wanted the priests to reveal their own lies.",
+        "answer": [
+            "The scribe."
+        ]
+    },
+    {
+        "output": "The bosses of Wilma's gang believe that Anthony Rogers will be useful to them in the current conflict because he has experience with the Han Empire and their technology, which will be useful in their fight against the Han Empire. They also believe that his unique perspective from having lived for 573 years will give him valuable insights into the situation.",
+        "answer": [
+            "Because he fought in the first world war."
+        ]
+    },
+    {
+        "output": "Soames describes a world that is more rigid and moralistic, with people being ghosts who are unconscious and automatic. He also describes a world where he is a ghost, but only a ghost, and not a physical, conscious being. He wishes he could revisit the world physically and consciously.",
+        "answer": [
+            "ones that are vague"
+        ]
+    },
+    {
+        "output": "It is not specified how long after Madame de Merret dies before people are allowed into the manor. The story only describes the events leading up to her death and the aftermath.",
+        "answer": [
+            "50 years"
+        ]
+    },
+    {
+        "output": "The Vervelle couple believe Grassou is the perfect match for their daughter.",
+        "answer": [
+            "Their daughter, Virgine. "
+        ]
+    },
+    {
+        "output": "Pierre Grassou.",
+        "answer": [
+            "Grassou"
+        ]
+    },
+    {
+        "output": "The slime enters Dana's apartment through the fireplace.",
+        "answer": [
+            "The slime gets into Dana's apartment from the bathtub."
+        ]
+    },
+    {
+        "output": "Elder Childers argues with Elder Simms about the location of the next trial.",
+        "answer": [
+            "That a mule bone is very powerful and can be a weapon."
+        ]
+    },
+    {
+        "output": "The bosses of Wilma's gang believe that Anthony Rogers will be useful to them in the current conflict because he has experience with the Han Empire and their technology, which will be useful in their fight against the Han Empire. They also believe that his unique perspective from having lived for 573 years will give him valuable insights into the situation.",
+        "answer": [
+            "Anthony Rogers has previous wartime and combatant experience."
+        ]
+    },
+    {
+        "output": "Reiko sees the figure from the tape in the photograph.",
+        "answer": [
+            "Her face is blurry"
+        ]
+    },
+    {
+        "output": "One.",
+        "answer": [
+            "Two"
+        ]
+    },
+    {
+        "output": "Mortimer Trefinnis was estranged from his siblings over a family quarrel about money.",
+        "answer": [
+            "because of the division of proceeds from selling the family business."
+        ]
+    },
+    {
+        "output": "2419.",
+        "answer": [
+            "2419."
+        ]
+    },
+    {
+        "output": "Bill ran to Texas to follow the harvest.",
+        "answer": [
+            "he murdered someone"
+        ]
+    },
+    {
+        "output": "Socrates compares going against the law to returning evil for evil and betraying the right.",
+        "answer": [
+            "Your parents"
+        ]
+    },
+    {
+        "output": "Lady Coxon is the aunt of Ruth Anvoy.",
+        "answer": [
+            "Lady Coxon is Ruth's aunt."
+        ]
+    },
+    {
+        "output": "Reiko is a high school student.",
+        "answer": [
+            "Reiko is a reporter."
+        ]
+    },
+    {
+        "output": "Socrates ultimately decides it is unjust to escape because he believes it would be dishonorable and would violate the agreements he made with the state. He values justice and the principles of the state more than his own life.",
+        "answer": [
+            "Because it violates his belief in the  laws of society."
+        ]
+    },
+    {
+        "output": "Drexl is killed by Alabama.",
+        "answer": [
+            "he is shot"
+        ]
+    },
+    {
+        "output": "Anvoy read the letter and then destroyed it.",
+        "answer": [
+            "She declined to read it."
+        ]
+    },
+    {
+        "output": "The teens had \"terrified\" expressions on their faces.",
+        "answer": [
+            "Twisted in fear"
+        ]
+    },
+    {
+        "output": "Soames gets upset with Beerbohm because Beerbohm mentions him in his memoir and portrays him as a failure. Soames feels that Beerbohm's portrayal is unfair and that he has been treated harshly. He also feels that Beerbohm has not been truthful about the events that took place. Soames is upset that Beerbohm has included him in his memoir and that Beerbohm has made him look ridiculous.",
+        "answer": [
+            "Beerbohm wrote a fictional story about Soames."
+        ]
+    },
+    {
+        "output": "One week.",
+        "answer": [
+            "Seven days"
+        ]
+    },
+    {
+        "output": "JOF.",
+        "answer": [
+            "all but Jof and the Juggler"
+        ]
+    },
+    {
+        "output": "Mary's mother filled her free time with reading and writing. She was a prolific author.",
+        "answer": [
+            "she was obsessed with novels."
+        ]
+    },
+    {
+        "output": "The question is not mentioned in the story.",
+        "answer": [
+            "HER BEAUTY"
+        ]
+    },
+    {
+        "output": "Falder was worried about Ruth because she had discovered his secret and he was afraid of the consequences.",
+        "answer": [
+            "Her husband abused her"
+        ]
+    },
+    {
+        "output": "Bill died in a car accident.",
+        "answer": [
+            "He is killed by police."
+        ]
+    },
+    {
+        "output": "To rest and recover from illness.",
+        "answer": [
+            "For Holmes' health."
+        ]
+    },
+    {
+        "output": "Vigo the Carpathian.",
+        "answer": [
+            "Vigo"
+        ]
+    },
+    {
+        "output": "Mary is initially educated to be a machine, with her education focused on acquiring superficial accomplishments and conforming to the expectations of society. She is not encouraged to develop her own thoughts and ideas.",
+        "answer": [
+            "Nature and books"
+        ]
+    },
+    {
+        "output": "The slime attack Dana and Oscar at the museum.",
+        "answer": [
+            "museum"
+        ]
+    },
+    {
+        "output": "The original cursed videotape was made by an elementary school kid who recorded a program on a VCR in a bed and breakfast in Izu. The tape contained a message that said \"You will die one week from now\" and the person who watched the tape died exactly one week later. The tape was later found to be cursed and anyone who watched it would die within a week. The identity of the person who made the tape is not revealed in the story.",
+        "answer": [
+            "Sadako Yamamura made the tape psionically."
+        ]
+    },
+    {
+        "output": "Athens.",
+        "answer": [
+            "In Socrates Cell?"
+        ]
+    },
+    {
+        "output": "Shizuko Yamamura died by suicide.",
+        "answer": [
+            "She committed suicide."
+        ]
+    },
+    {
+        "output": "Bennett Landsmann needed to defend Seth Lazurus in court because he was accused of stealing a rare book.",
+        "answer": [
+            "because he's a lawyer"
+        ]
+    },
+    {
+        "output": "Enoch Soames was known for his eccentric and flamboyant clothing choices.",
+        "answer": [
+            "always wore a grey waterproof cape and a soft black hat"
+        ]
+    },
+    {
+        "output": "Baron Conrad killed his own brother Frederick.",
+        "answer": [
+            "Baron Frederick"
+        ]
+    },
+    {
+        "output": "Mary is initially educated to be a machine, with her education focused on acquiring superficial accomplishments and conforming to the expectations of society. She is not encouraged to develop her own thoughts and ideas.",
+        "answer": [
+            "She educates herself using books and the natural world."
+        ]
+    },
+    {
+        "output": "Baptists.",
+        "answer": [
+            "The Baptist's supported Dave. "
+        ]
+    },
+    {
+        "output": "Vane writes about her experiences in Paris and her travels in Europe.",
+        "answer": [
+            "Louis Leverett"
+        ]
+    },
+    {
+        "output": "The story of Otto of the Silver Hand by Howard Pyle explores themes of loyalty, honor, revenge, justice, and the power of forgiveness. It also highlights the importance of wisdom, compassion, and the lessons learned from the past. The story shows how Otto's experiences shape his character and values, leading him to become a wise and compassionate leader. The themes of loyalty and honor are particularly prominent as Otto seeks to avenge his father's death and protect his family. The story also emphasizes the importance of forgiveness and reconciliation,",
+        "answer": [
+            "Be responsible,keep your word and be wise and peaceful."
+        ]
+    },
+    {
+        "output": "Tomoko becomes increasingly distressed and eventually jumps in her seat when Masami points at the TV.",
+        "answer": [
+            "Tomoko reveals that they watched a video tape with several friends a week ago and received a strange call after watching it. Tomoko is killed while Masami watches."
+        ]
+    },
+    {
+        "output": "The teens had \"terrified\" expressions on their faces.",
+        "answer": [
+            "Their faces twisted in fear."
+        ]
+    },
+    {
+        "output": "The final raid in the story was on the Hans.",
+        "answer": [
+            "The Sinsings"
+        ]
+    },
+    {
+        "output": "Abby.",
+        "answer": [
+            "His girlfriend Abby and her sister Linda."
+        ]
+    },
+    {
+        "output": "The Ghostbusters do not intervene in the argument between Ray and Winston. They instead let the argument continue.",
+        "answer": [
+            "Take their clothes off"
+        ]
+    },
+    {
+        "output": "Frank Saltram is a writer.",
+        "answer": [
+            "He does not work."
+        ]
+    },
+    {
+        "output": "She planned to escape with her lover, James James.",
+        "answer": [
+            "Falder"
+        ]
+    },
+    {
+        "output": "None is mentioned in the story. The story focuses on the character of Doctor Nordenfeld and his work on the _Star Queen_ spacecraft. There is no mention of Laura or any medical school she attends. The story is about the discovery of a virus called chlorophage and the efforts to combat it.",
+        "answer": [
+            "Laura attends Harvard Medical school."
+        ]
+    },
+    {
+        "output": "The mayor gave the Ghostbusters a key to the city.",
+        "answer": [
+            "key to the city"
+        ]
+    },
+    {
+        "output": "The Witch creates a world of dreams and visions.",
+        "answer": [
+            "Hermaphroditus."
+        ]
+    },
+    {
+        "output": "In a secret room.",
+        "answer": [
+            "In a closet."
+        ]
+    },
+    {
+        "output": "JEZZIE.",
+        "answer": [
+            "His son Gabe"
+        ]
+    },
+    {
+        "output": "The story does not specify where Jim and Dave are from. The story is set in a small town in the South, but does not mention the specific location.",
+        "answer": [
+            "Eatonville"
+        ]
+    },
+    {
+        "output": "Dr. Sterndale.",
+        "answer": [
+            "The housekeeper"
+        ]
+    },
+    {
+        "output": "The DEMONS.",
+        "answer": [
+            "One of his fellow soldiers."
+        ]
+    },
+    {
+        "output": "The story does not mention Landsmann's profession. It only describes his experiences and actions during the events of the story. So, it is not possible to determine his profession based on the story alone.",
+        "answer": [
+            "A lawyer"
+        ]
+    },
+    {
+        "output": "Gravener wants Ruth to use the money to help him win back his wife. He believes that if he can show his wife that he has the means to support her, she will forgive him and they can be reconciled. He is willing to sacrifice his own happiness for the sake of his wife and the Coxon Fund.",
+        "answer": [
+            "Keep it to buy a house "
+        ]
+    },
+    {
+        "output": "Miranda writes her letters to her mother.",
+        "answer": [
+            "Her mother."
+        ]
+    },
+    {
+        "output": "One week.",
+        "answer": [
+            "7 days"
+        ]
+    },
+    {
+        "output": "Vervelle.",
+        "answer": [
+            "Virginie."
+        ]
+    },
+    {
+        "output": "Reiko makes a copy of the cursed video and shows it to someone else, breaking the curse.",
+        "answer": [
+            "She made a copy of the tape and gives it to her husband to study."
+        ]
+    },
+    {
+        "output": "Death is disguised as a man in black.",
+        "answer": [
+            "A monk."
+        ]
+    },
+    {
+        "output": "They agree to be governed by the laws of Athens.",
+        "answer": [
+            "Comply with the laws"
+        ]
+    },
+    {
+        "output": "Falder breaks his neck by jumping from a window.",
+        "answer": [
+            "He throws himself down the stairs"
+        ]
+    },
+    {
+        "output": "Making a copy of the tape and showing it to someone else within a week.",
+        "answer": [
+            "Making a copy and showing it to someone else"
+        ]
+    },
+    {
+        "output": "Brenda.",
+        "answer": [
+            "Brenda."
+        ]
+    },
+    {
+        "output": "They waited for the stranger to come and then left for Paris.",
+        "answer": [
+            "They stayed in the bedroom for a few days with the sounds of her lover trapped. "
+        ]
+    },
+    {
+        "output": "Mary spent most of her time outside of the home in the company of her friend Ann. They would often go on walks and visit places of interest. They also engaged in activities like reading and playing music.",
+        "answer": [
+            "With her charity"
+        ]
+    },
+    {
+        "output": "Clifford is a film producer.",
+        "answer": [
+            "A security guard."
+        ]
+    },
+    {
+        "output": "Rodgers was exposed to the Sinsing Gang's \"degenerate\" civilization.",
+        "answer": [
+            "Radioactive gas"
+        ]
+    },
+    {
+        "output": "The rental cabin is located in Mt. Mihara.",
+        "answer": [
+            "Izu Oshima Island"
+        ]
+    },
+    {
+        "output": "Clarence takes the \"Sgt. Fury\" comic book that he bought for Alabama.",
+        "answer": [
+            "A bag that contains cocaine"
+        ]
+    },
+    {
+        "output": "Soames was sent 100 years into the future.",
+        "answer": [
+            "100."
+        ]
+    },
+    {
+        "output": "The housekeeper's feet got wet when she went to the cellar to fetch a jug of water and slipped on the wet floor.",
+        "answer": [
+            "Holmes accidentally kicks over a water pot"
+        ]
+    },
+    {
+        "output": "A corrupter of the laws.",
+        "answer": [
+            "He would turn into an outlaw."
+        ]
+    },
+    {
+        "output": "James How.",
+        "answer": [
+            "Frome"
+        ]
+    },
+    {
+        "output": "The Mayor.",
+        "answer": [
+            "Dana"
+        ]
+    },
+    {
+        "output": "Soames is unhappy about his failure to make an impact on the literary world during the 1890s. He feels that he was overlooked and that his talents were not recognized. This leads to a sense of failure and disappointment.",
+        "answer": [
+            "the his works are not recognized"
+        ]
+    },
+    {
+        "output": "Eliza.",
+        "answer": [
+            "Eliza"
+        ]
+    },
+    {
+        "output": "The Sinsing Gang.",
+        "answer": [
+            "The Hans"
+        ]
+    },
+    {
+        "output": "In a bar.",
+        "answer": [
+            "At a Detroit theater. "
+        ]
+    },
+    {
+        "output": "Oscar is Dana's son.",
+        "answer": [
+            "It's her son"
+        ]
+    },
+    {
+        "output": "Socrates believes that he will be judged as an enemy in the afterlife if he breaks agreements and returns evil for evil.",
+        "answer": [
+            "It would have a negative effect"
+        ]
+    },
+    {
+        "output": "She threatened to leave her husband if he did not give her a divorce.",
+        "answer": [
+            "Opened the closet"
+        ]
+    },
+    {
+        "output": "Doctor Nordenfeld.",
+        "answer": [
+            "Seth Lazarus"
+        ]
+    },
+    {
+        "output": "The art-dealer describes Grassou's skill level as \"a great artist.\"",
+        "answer": [
+            "A grand master."
+        ]
+    },
+    {
+        "output": "Grassou's biggest disappointment is that his true works of art are not recognized and appreciated by the public or critics. He feels that his name is a term of contempt in the studios and that artists laugh at his work. He also feels that the feuilletons take no notice of his pictures. Despite this, he continues to work and aims for the Academy, where he hopes to gain recognition. However, he also secretly buys the pictures of celebrated artists who are pinched for means and substitutes them for the wretched daubs in the collection at Ville d'Avray.",
+        "answer": [
+            "He doesn't feel he is a true artist."
+        ]
+    },
+    {
+        "output": "Vincenzo Coccotti works for the Mafia.",
+        "answer": [
+            "A mobster named \"Blue Lou Boyle\". "
+        ]
+    },
+    {
+        "output": "The police came back for Falder after he left prison because they had discovered that he had been involved in a fraudulent scheme. They wanted to question him about it.",
+        "answer": [
+            "He failed to report"
+        ]
+    },
+    {
+        "output": "Ruth Honeywill.",
+        "answer": [
+            "Ruth Honeywill"
+        ]
+    },
+    {
+        "output": "In a trailer.",
+        "answer": [
+            "Mexico."
+        ]
+    },
+    {
+        "output": "Eliza requests that Mary promise to take care of her husband and children.",
+        "answer": [
+            "That she marry Charles"
+        ]
+    },
+    {
+        "output": "Death asks Antonius if he is prepared.",
+        "answer": [
+            "If Antonius has succeeded in his \"one meaningful deed.\""
+        ]
+    },
+    {
+        "output": "The story does not mention a murder trial or any specific defense. It only describes Lazarus and his actions. So there is no one defending Lazarus during a trial. The story focuses on Lazarus and his actions, not a trial or legal proceedings.",
+        "answer": [
+            "bennett landsmann"
+        ]
+    },
+    {
+        "output": "Mary learns to struggle for resignation.",
+        "answer": [
+            "accomplishments"
+        ]
+    },
+    {
+        "output": "Death is disguised as a man in black.",
+        "answer": [
+            "a priest"
+        ]
+    },
+    {
+        "output": "Mary married Charles to fulfill a promise to her husband that she would live with him if he allowed her to travel for a year. She also hoped that the time would help her overcome her disgust for him. However, the marriage was not successful and Mary struggled to find happiness.",
+        "answer": [
+            "Because her mother requested it on her deathbed."
+        ]
+    },
+    {
+        "output": "Mortimer blames himself for the death of his sister.",
+        "answer": [
+            "the devil"
+        ]
+    },
+    {
+        "output": "Jacob first meets Michael Newman in the hospital in Vietnam.",
+        "answer": [
+            "He treated him in Vietnam."
+        ]
+    },
+    {
+        "output": "American.",
+        "answer": [
+            "American"
+        ]
+    },
+    {
+        "output": "The Sinsing gang's airship.",
+        "answer": [
+            "The Han gang."
+        ]
+    },
+    {
+        "output": "When she realizes that the figure in the room is wearing Ryuji's clothes.",
+        "answer": [
+            "After her husband calls her"
+        ]
+    },
+    {
+        "output": "Miss Anvoy.",
+        "answer": [
+            "Ruth Anvoy"
+        ]
+    },
+    {
+        "output": "They go to Sanford.",
+        "answer": [
+            "They both return to Eatonville, Florida. "
+        ]
+    },
+    {
+        "output": "The story is set in the Middle Ages in a castle called Drachenhausen and the surrounding countryside. The castle is ruled by a robber baron named Casimir and his family. The story follows the adventures of a young boy named Otto who lives in the castle and eventually escapes with the help of a one-eyed man named Hans. The story also involves the Emperor Rudolph and the town of Nuremberg. The setting is characterized by the dark and superstitious atmosphere of the Middle Ages, with the castle and its inhabitants being surrounded by a forest and a river. The story",
+        "answer": [
+            "Germany"
+        ]
+    },
+    {
+        "output": "Reiko's husband dies.",
+        "answer": [
+            "has a heart attack"
+        ]
+    },
+    {
+        "output": "Falder worked as a clerk in the office of James and Walter How.",
+        "answer": [
+            "James How & Son "
+        ]
+    },
+    {
+        "output": "Saltram is living with the Mulvilles.",
+        "answer": [
+            "Lives with mulvilles "
+        ]
+    },
+    {
+        "output": "In a secret room.",
+        "answer": [
+            "In the closet"
+        ]
+    },
+    {
+        "output": "Grassou is still resentful despite his potentially advantageous marriage because he feels that his artistic talent is not fully recognized and respected by society. He is unhappy with the way his work is perceived and valued.",
+        "answer": [
+            "He feels as the he is still not a real artist."
+        ]
+    },
+    {
+        "output": "The teenagers were discussing a rumor about a curse that would cause the death of the person who watched a video within a week of recording it.",
+        "answer": [
+            "A videotape which is cursed and is rumored to bear a curse that kills the viewer in seven days after watching."
+        ]
+    },
+    {
+        "output": "Alabama was a prostitute.",
+        "answer": [
+            "Alabama was a call girl."
+        ]
+    },
+    {
+        "output": "Jezzie was a demon that Jake Singer encountered.",
+        "answer": [
+            "The woman he dated and lived with after the war."
+        ]
+    },
+    {
+        "output": "Louis tells Jacob that Hell burns away his dark side.",
+        "answer": [
+            "Your memories and attachments."
+        ]
+    },
+    {
+        "output": "The contract with the state.",
+        "answer": [
+            "The social contract."
+        ]
+    },
+    {
+        "output": "The art-dealer describes Grassou's skill level as \"a great artist.\"",
+        "answer": [
+            "he calls Grassou a grand master"
+        ]
+    },
+    {
+        "output": "The Mayor, Joe Clark.",
+        "answer": [
+            "Elder Childers"
+        ]
+    },
+    {
+        "output": "Jim was sentenced to a public whipping.",
+        "answer": [
+            "Jim was banished from his town for two years."
+        ]
+    },
+    {
+        "output": "Eliza favors her son.",
+        "answer": [
+            "Eliza favors her son over her daughter."
+        ]
+    },
+    {
+        "output": "Otto is known for his wisdom and pure, simple wisdom that the old monks of the White Cross on the hill had taught him.",
+        "answer": [
+            "His wise counsel and peaceful nature"
+        ]
+    },
+    {
+        "output": "Eliza's favorite is Henry.",
+        "answer": [
+            "Her son"
+        ]
+    },
+    {
+        "output": "Buck Rogers was investigating reports of phenomena in the vicinity of the abandoned coal mines in Pennsylvania.",
+        "answer": [
+            "Wyoming Valley"
+        ]
+    },
+    {
+        "output": "T.K. Nupton did not believe in Soames' existence.",
+        "answer": [
+            "He thought he was a fictional character."
+        ]
+    },
+    {
+        "output": "Mortimer Trefinnis was estranged from his siblings over a family quarrel about money.",
+        "answer": [
+            "The matter of the division of the proceeds from selling the family business."
+        ]
+    },
+    {
+        "output": "In a basement.",
+        "answer": [
+            "Hawaii"
+        ]
+    },
+    {
+        "output": "Pierre Grassou's daughter.",
+        "answer": [
+            "Pierre Grassou."
+        ]
+    },
+    {
+        "output": "He discovered that she had a lover and planned to run away with him.",
+        "answer": [
+            "That she was having an affair. "
+        ]
+    },
+    {
+        "output": "The hidden message is that the curse can be broken by making a copy of the video and showing it to someone else within a week.",
+        "answer": [
+            "Frolic in brine, goblins be thine"
+        ]
+    },
+    {
+        "output": "A mysterious, abandoned house.",
+        "answer": [
+            "The ruins of an abandoned manor. "
+        ]
+    },
+    {
+        "output": "The Coxon Fund was established and Saltram began to draw the income. However, the income was not enough to sustain him and he began to decline. The story ends with the narrator reflecting on how the fund was a great boon but also a source of unhappiness for the people involved.",
+        "answer": [
+            "They broke off their engagement."
+        ]
+    },
+    {
+        "output": "William Platt.",
+        "answer": [
+            "Violet Ray."
+        ]
+    },
+    {
+        "output": "The preacher.",
+        "answer": [
+            "Bill"
+        ]
+    },
+    {
+        "output": "Masami and Tomoko.",
+        "answer": [
+            "Masami and Tomoko"
+        ]
+    },
+    {
+        "output": "The story does not mention any war. The character Rogers learned his battle knowledge through his experiences in the 25th Century. The story is set in the 25th Century.",
+        "answer": [
+            "World War 1, or The Great War"
+        ]
+    },
+    {
+        "output": "Grassou discovers that Vervelle has purchased many of his paintings from Elie Magus and that he has been paid less than the market value for them.",
+        "answer": [
+            "A number of his own forgeries."
+        ]
+    },
+    {
+        "output": "The event that reunites Jacob with the other men from his platoon is not specified in the story. The story ends with Jacob being taken away by the military and the other men not knowing what happened to him. The story does not provide any information about a reunion.",
+        "answer": [
+            "Paul's death."
+        ]
+    },
+    {
+        "output": "Socrates compares citizens to the laws of the state.",
+        "answer": [
+            "Citizens are bound to laws the same as children are bound to parents."
+        ]
+    },
+    {
+        "output": "A coffin.",
+        "answer": [
+            "A coffin."
+        ]
+    },
+    {
+        "output": "2419.",
+        "answer": [
+            "2419"
+        ]
+    },
+    {
+        "output": "Otto is welcomed back to his father's castle by his mother and sister.",
+        "answer": [
+            "The nephew of Baron Frederick storms the castle and kidnaps Otto"
+        ]
+    },
+    {
+        "output": "The Wise Guys and the police have a standoff.",
+        "answer": [
+            "Elliot accidentally reveals himself as an informant."
+        ]
+    },
+    {
+        "output": "The story does not mention a specific town. It only describes a porch and store in front of a store in a small town. The characters are not identified as being from any specific town. So, it is not possible to determine the town that Daisy encountered Jim in.",
+        "answer": [
+            "Story did not mention"
+        ]
+    },
+    {
+        "output": "The owner of the manor is unknown. The house is abandoned and there is no one living there. It is a mystery.",
+        "answer": [
+            "Madame de Merret"
+        ]
+    },
+    {
+        "output": "Gravener was urging Anvoy to accept a marriage proposal from him.",
+        "answer": [
+            "Keep the money."
+        ]
+    },
+    {
+        "output": "Soames would find out whether his poetic talent would have been recognized in the future by reading a book titled \"The Yellow Book\" in the year 1897. The book would contain a title page with his name and a dedication to him. The title page would be a \"guaranteed proof\" that his poetic talent would have been recognized in the future. The book would be published in June 1897, and Soames would be able to read it in the reading room in Piccadilly. The title page would be a \"guaranteed proof\" that his poetic talent",
+        "answer": [
+            "By selling his soul to the devil."
+        ]
+    },
+    {
+        "output": "Apis.",
+        "answer": [
+            "Apis"
+        ]
+    },
+    {
+        "output": "Two people fainted after seeing Brenda's dead body.",
+        "answer": [
+            "2"
+        ]
+    },
+    {
+        "output": "Alabama.",
+        "answer": [
+            "Alabama Whitman"
+        ]
+    },
+    {
+        "output": "Chuck kills the farmer with a screwdriver.",
+        "answer": [
+            "Bill"
+        ]
+    },
+    {
+        "output": "Elie Magus.",
+        "answer": [
+            "The greats"
+        ]
+    },
+    {
+        "output": "\"Cuban Pete\"",
+        "answer": [
+            "Auld Lang Syne"
+        ]
+    },
+    {
+        "output": "Grassou believes that artists laugh at his work and that his name is a term of contempt in the studios.",
+        "answer": [
+            "That artists laugh at and ridicule his work."
+        ]
+    },
+    {
+        "output": "Mary marries her husband.",
+        "answer": [
+            "Charles"
+        ]
+    },
+    {
+        "output": "A mysterious, abandoned house.",
+        "answer": [
+            "Abandon manor"
+        ]
+    },
+    {
+        "output": "\"We came from Ostrov in Eastern Poland.\"",
+        "answer": [
+            "Auld Lang Saye"
+        ]
+    },
+    {
+        "output": "La Grande Breteche is an abandoned manor located on the banks of the Loir, about 100 paces from Vendome. It is isolated and surrounded by a garden and a high fence of willows. The house is in a state of disrepair and the doors and windows are always closed. The roof is dilapidated and the shutters are always closed. The place is overgrown with weeds and the walls are scored by enormous cracks. The bell-cord is rotten and the gutter-spouts are broken. The house is a vast enigma with",
+        "answer": [
+            "Near the town Vendome"
+        ]
+    },
+    {
+        "output": "They fought over Daisy.",
+        "answer": [
+            "For Daisy's affection. "
+        ]
+    },
+    {
+        "output": "The Ladder was created by Jake Singer.",
+        "answer": [
+            "The US Army chemical weapons division."
+        ]
+    },
+    {
+        "output": "The story does not mention Baroness Matillda going into premature labor. The story mainly focuses on the adventures of Otto, a young boy who becomes a hero and the right hand of the Emperor Rudolph. The story does not provide details about the Baroness's pregnancy or labor. The story mainly focuses on Otto's growth and adventures in the Emperor's court. So the question does not apply to the story.",
+        "answer": [
+            "Because she saw her husband's battle wounds"
+        ]
+    },
+    {
+        "output": "The less beautiful received strange panacea in a crystal bowl from the Witch.",
+        "answer": [
+            "Strange dreams."
+        ]
+    },
+    {
+        "output": "In a villa with a garden.",
+        "answer": [
+            "In a mansion in Ville-d'Avray. "
+        ]
+    },
+    {
+        "output": "Baron Henry attacks Castle Drachenhausen to avenge the death of his father, who was killed by the Roderburgs.",
+        "answer": [
+            "Baron Conrad killed his uncle, Baron Frederick."
+        ]
+    },
+    {
+        "output": "Bill died in a car accident.",
+        "answer": [
+            "he was killed by the police"
+        ]
+    },
+    {
+        "output": "The Mayor, Joe Clark.",
+        "answer": [
+            "Elder Simms"
+        ]
+    },
+    {
+        "output": "Soames's presence in the future caused a sensation and drew attention. People were curious and followed him around, seeming afraid of him. The effect was \"awful.\" However, Soames himself was not physically present in the future, but rather a projection of his ghostly self. He was a \"guaranteed, proved ghost.\" The crowd that Soames encountered in the future was made up of solid, palpable, vocal, but unconscious and automatic ghosts. The building in which Soames was projected was itself an illusion. The next time Soames would only be a",
+        "answer": [
+            "It caused a stir."
+        ]
+    },
+    {
+        "output": "Chuck.",
+        "answer": [
+            "Abby"
+        ]
+    },
+    {
+        "output": "The mayor wants to have the Ghostbusters come to the city.",
+        "answer": [
+            "Governor"
+        ]
+    },
+    {
+        "output": "The phonetic spelling of the 'future' article is unique in that it uses the IPA (International Phonetic Alphabet) to represent the sounds of the words. This is different from the standard spelling used in English.",
+        "answer": [
+            "The English lanuage has evolved."
+        ]
+    },
+    {
+        "output": "The drug that Michael created causes people to transform into demons.",
+        "answer": [
+            "It makes people crazy and homicidal"
+        ]
+    },
+    {
+        "output": "Mary and Ann meet a man named Henry in Lisbon.",
+        "answer": [
+            "The duo meet Henry on their travels to Lisbon."
+        ]
+    },
+    {
+        "output": "In the bathroom.",
+        "answer": [
+            "In the hotel."
+        ]
+    },
+    {
+        "output": "The lawyer discovered that Jacob and his unit were involved in a secret government project involving mind control experiments.",
+        "answer": [
+            "They were never deployed and were discharged for psychological reasons."
+        ]
+    },
+    {
+        "output": "Madame de Merret asks the mason to leave a crack at the bottom of the door.",
+        "answer": [
+            "Break a piece of the door that she may look on her lover one last time"
+        ]
+    },
+    {
+        "output": "The Witch lives on Atlas' mountain within a cavern, by a secret fountain.",
+        "answer": [
+            "Witch lives in cave on Atlas Mountains"
+        ]
+    },
+    {
+        "output": "Otto takes refuge in the White Cross on the Hill.",
+        "answer": [
+            "The monastery where he grew up."
+        ]
+    },
+    {
+        "output": "A corrupter of the laws.",
+        "answer": [
+            "An outlaw"
+        ]
+    },
+    {
+        "output": "Leon focuses on his experiences and observations in writing his letters.",
+        "answer": [
+            "The beauty of the women"
+        ]
+    },
+    {
+        "output": "The Witch perceived the inner form of mankind, often seeing the beauty of the soul beneath the rough and worn disguises of the physical body.",
+        "answer": [
+            "The fears and desires of mankind."
+        ]
+    },
+    {
+        "output": "Bill worked in a factory.",
+        "answer": [
+            "Bill worked in a steel mill."
+        ]
+    },
+    {
+        "output": "Miranda Hope.",
+        "answer": [
+            "the German professor"
+        ]
+    },
+    {
+        "output": "The check was forged for 10 pounds.",
+        "answer": [
+            "Ninety pounds"
+        ]
+    },
+    {
+        "output": "The housekeeper's feet got wet when she went to the cellar to fetch a jug of water and slipped on the wet floor.",
+        "answer": [
+            "Sherlock had wet the floor by overturning a water-filled pot."
+        ]
+    },
+    {
+        "output": "Reiko's husband dies.",
+        "answer": [
+            "He watches the video and dies of Cardiac arrest."
+        ]
+    },
+    {
+        "output": "The story does not mention a specific town. It only describes a porch and store in front of a store in a small town. The characters are not identified as being from any specific town. So, it is not possible to determine the town that Daisy encountered Jim in.",
+        "answer": [
+            "outside town"
+        ]
+    },
+    {
+        "output": "Mortimer Tregennis.",
+        "answer": [
+            "Brenda Tregennis"
+        ]
+    },
+    {
+        "output": "Holmes observes the remains of a fire in the fireplace.",
+        "answer": [
+            "The remains of afire in the fireplace."
+        ]
+    },
+    {
+        "output": "Anthony Rogers was working for a company that manufactured explosive rockets.",
+        "answer": [
+            "Radioactive gas"
+        ]
+    },
+    {
+        "output": "JOF.",
+        "answer": [
+            "Everyone except for Jof"
+        ]
+    },
+    {
+        "output": "When she realizes that the figure in the room is wearing Ryuji's clothes.",
+        "answer": [
+            "She received a call from Ryuji during his last minutes alive."
+        ]
+    }
+]

data/results/longbench/test/generation/qasper.json

@@ -0,0 +1,1352 @@
+[
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Ground truth is not established in the paper"
+        ]
+    },
+    {
+        "output": "GhostVLAD is an extension of the NetVLAD approach, which is used for language identification. It adds Ghost clusters along with the NetVLAD clusters to map any noisy or irrelevant content into ghost clusters and is not included during the feature aggregation stage. Due to this, during feature aggregation, the contribution of the noisy and unwanted features to normal VLAD clusters are assigned less weights while Ghost clusters absorb most of the weight.",
+        "answer": [
+            "extension of the NetVLAD, adds Ghost clusters along with the NetVLAD clusters",
+            "An extension of NetVLAD which replaces hard assignment-based clustering with soft assignment-based clustering with the additon o fusing Ghost clusters to deal with noisy content."
+        ]
+    },
+    {
+        "output": "The proposed model outperforms the state of the art results by 68.8% to 71.8%.",
+        "answer": [
+            "the attention model, MDREA, also outperforms the best existing research results (WAP 0.690 to 0.688)"
+        ]
+    },
+    {
+        "output": "The article proposes the use of context tweets, which provide additional information about the tweet being analyzed. The authors suggest that computers can benefit from taking context tweets into account in detecting abusive language.",
+        "answer": [
+            "using tweets that one has replied or quoted to as contextual information",
+            "text sequences of context tweets"
+        ]
+    },
+    {
+        "output": "They looked at FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney.",
+        "answer": [
+            "FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney",
+            "FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney."
+        ]
+    },
+    {
+        "output": "No. The article mentions that the hashtag dataset includes tweets in English, Spanish, French, German, Italian, Portuguese, and Chinese. The SemEval dataset is an English language benchmark dataset. However, the article does not mention if the hashtag segmentation model is trained on all these languages or only English.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "The article does not mention any specific evaluation method for the concept-map-based MDS task.",
+        "answer": [
+            "Answer with content missing: (Evaluation Metrics section) Precision, Recall, F1-scores, Strict match, METEOR, ROUGE-2"
+        ]
+    },
+    {
+        "output": "The XSum dataset is used for evaluation.",
+        "answer": [
+            "CNN/DailyMail news highlights, New York Times Annotated Corpus, XSum",
+            "the CNN/DailyMail news highlights dataset BIBREF24, the New York Times Annotated Corpus (NYT; BIBREF25), XSum BIBREF22"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "GM$\\_$KL achieves better correlation than existing approaches for various metrics on SCWS dataset."
+        ]
+    },
+    {
+        "output": "The authors introduce a new dataset called BookTest, which is similar to the Children's Book Test but more than 60 times larger. They show that training on the new dataset improves the model's accuracy on the Children's Book Test. They also discuss the challenges of training on large datasets and the importance of speeding up the algorithm.",
+        "answer": [
+            "simply averaging the predictions from the constituent single models"
+        ]
+    },
+    {
+        "output": "The EmotionLines dataset is composed of two subsets, Friends and EmotionPush, which are sourced from the scripts of the Friends TV sitcom and Facebook messenger chats, respectively.",
+        "answer": [
+            "Friends TV sitcom, Facebook messenger chats"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "English",
+            "Simple English"
+        ]
+    },
+    {
+        "output": "IMDb dataset",
+        "answer": [
+            "IMDb dataset of movie reviews",
+            "IMDb"
+        ]
+    },
+    {
+        "output": "The proposed system achieves an F1 score of 85.23 on the DL-PS dataset, 82.57 on the EC-MT dataset, and 88.55 on the EC-UQ dataset.",
+        "answer": [
+            "F1 scores of 85.99 on the DL-PS data,  75.15 on the EC-MT data and 71.53 on the EC-UQ data ",
+            "F1 of 85.99 on the DL-PS dataset (dialog domain); 75.15 on EC-MT and 71.53 on EC-UQ (e-commerce domain)"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "No"
+        ]
+    },
+    {
+        "output": "The article mentions the use of the IBM Watson Dialog System, which is a publicly available dataset of multi-turn conversations. It also describes the use of the IBM Watson Turing Test, which is a dataset of text-based conversations. However, the article does not provide specific details on the datasets used for the finance advisory system.",
+        "answer": [
+            "Custom dataset with user questions; set of documents, twitter posts and news articles, all related to finance.",
+            "a self-collected financial intents dataset in Portuguese"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide information on which stock market sector achieved the best performance.",
+        "answer": [
+            "Energy with accuracy of 0.538",
+            "Energy"
+        ]
+    },
+    {
+        "output": "They compared the RNN-based NMT model, Transformer-NMT model and SMT model.",
+        "answer": [
+            "RNN-based NMT model, Transformer-NMT"
+        ]
+    },
+    {
+        "output": "The three regularization terms are: (1) a regularization term associated with neutral features; (2) the maximum entropy of class distribution regularization term; and (3) the KL divergence between reference and predicted class distribution.",
+        "answer": [
+            "a regularization term associated with neutral features, the maximum entropy of class distribution regularization term, the KL divergence between reference and predicted class distribution",
+            "a regularization term associated with neutral features,  the maximum entropy of class distribution, KL divergence between reference and predicted class distribution"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide enough information to answer this question.",
+        "answer": [
+            "SVM with unigram, bigram, and trigram features, SVM with average word embedding, SVM with average transformed word embeddings, CNN, ecurrent Convolutional Neural Networks, SVM and deep learning models with comment information",
+            "SVM with unigram, bigram, trigram features, with average word embedding, with average transformed word embeddings, CNN and RCNN, SVM, CNN, RCNN with comment information"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "They decrease MAE in 0.34"
+        ]
+    },
+    {
+        "output": "The authors propose using sparse normalizing transforms like sparsemax in attention mechanisms to improve interpretability. This allows for exactly zero probabilities for irrelevant words. The adaptive version of sparse attention allows the shape of each attention head to be learnable and dynamic, enabling more specialized attention heads. The authors identify both crisper examples of attention head behavior and novel behaviors unraveled thanks to the sparsity and adaptivity of their proposed model.",
+        "answer": [
+            "the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence",
+            "We introduce sparse attention into the Transformer architecture"
+        ]
+    },
+    {
+        "output": "The baseline was a context-agnostic machine translation system.",
+        "answer": [
+            " MT system on the data released by BIBREF11",
+            "Transformer base, two-pass CADec model"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not mention any specific evaluation metrics.",
+        "answer": [
+            "translation probabilities, Labeled Attachment Scores (LAS)",
+            "accuracy, Labeled Attachment Scores (LAS)"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "the model is pre-trained on CTC-based ASR task and MT task in the pre-training stage."
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "The encoder has a character-based architecture.",
+        "answer": [
+            "LSTM",
+            "LSTM"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Unanswerable",
+            "Yes"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "Unanswerable",
+            "LF-MMI Attention\nSeq2Seq \nRNN-T \nChar E2E LF-MMI \nPhone E2E LF-MMI \nCTC + Gram-CTC"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide information on the number of users they look at.",
+        "answer": [
+            "22,880 users",
+            "20,000"
+        ]
+    },
+    {
+        "output": "Perplexity, user-ranking, and coherence metrics are used for evaluation.",
+        "answer": [
+            "Byte-Pair Encoding perplexity  (BPE PPL),\nBLEU-1,\nBLEU-4,\nROUGE-L,\npercentage of distinct unigram (D-1),\npercentage of distinct bigrams(D-2),\nuser matching accuracy(UMA),\nMean Reciprocal Rank(MRR)\nPairwise preference over baseline(PP)",
+            "BLEU-1/4 and ROUGE-L, likelihood of generated recipes using identical input specifications but conditioned on ten different user profiles, user matching accuracy (UMA), Mean Reciprocal Rank (MRR), neural scoring model from BIBREF33 to measure recipe-level coherence",
+            " Distinct-1/2, UMA = User Matching Accuracy, MRR\n= Mean Reciprocal Rank, PP = Pairwise preference over baseline (evaluated for 310 recipe pairs per model)"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "(1) the time the patient has been experiencing the symptom, (2) activities that trigger the symptom (to occur or worsen), (3) the extent of seriousness, (4) the frequency occurrence of the symptom, and (5) the location of symptom, No Answer",
+            "the time the patient has been experiencing the symptom, activities that trigger the symptom, the extent of seriousness, the frequency occurrence of the symptom, the location of symptom, 9 symptoms"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "57,505 sentences",
+            "57,505 sentences"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "four machine translation tasks: German -> English, Japanese -> English, Romanian -> English, English -> German",
+            " four machine translation tasks, IWSLT 2017 German $\\rightarrow $ English BIBREF27, KFTT Japanese $\\rightarrow $ English BIBREF28, WMT 2016 Romanian $\\rightarrow $ English BIBREF29, WMT 2014 English $\\rightarrow $ German BIBREF30"
+        ]
+    },
+    {
+        "output": "Improvement in performance for Estonian in the NER task is not mentioned in the article.",
+        "answer": [
+            "5 percent points.",
+            "0.05 F1"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "No",
+            "No"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Nguni languages (zul, xho, nbl, ssw), Sotho languages (nso, sot, tsn)",
+            "The Nguni languages are similar to each other, The same is true of the Sotho languages"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "Unidirectional LSTM networks with 2, 6, 7, 8, and  9 layers."
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "a sample of  29,794 wikipedia articles and 2,794 arXiv papers "
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide any information on how the human judgements were assembled.",
+        "answer": [
+            "50 human annotators ranked a random sample of 100 translations by Adequacy, Fluency and overall ranking on a 5-point scale.",
+            "adequacy, precision and ranking values"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "The models are evaluated based on their ability to accurately reconstruct the target sequence given a set of keywords. The efficiency of the model is measured by the retention rate of tokens, which is the fraction of tokens that are kept in the keywords. The accuracy of the model is measured by the fraction of sentences generated by greedily decoding the model that exactly matches the target sentence.",
+        "answer": [
+            "by training an autocomplete system on 500K randomly sampled sentences from Yelp reviews",
+            "efficiency of a communication scheme $(q_{\\alpha },p_{\\beta })$ by the retention rate of tokens, which is measured as the fraction of tokens that are kept in the keywords, accuracy of a scheme is measured as the fraction of sentences generated by greedily decoding the model that exactly matches the target sentence"
+        ]
+    },
+    {
+        "output": "Precision, recall, and F-measure.",
+        "answer": [
+            "Precision, Recall, F-measure, accuracy",
+            "Precision, Recall and F-measure"
+        ]
+    },
+    {
+        "output": "The source domain is the domain with labeled data, and the target domain is the domain with very few or no labeled data. The article discusses the problem of cross-domain sentiment classification, where knowledge in the source domain is transferred to the target domain to alleviate the required labeling effort. The intersection between the vocabulary of reviews in the source and target domains is small, making domain adaptation difficult. The article proposes a novel framework to better exploit unlabeled target data for domain adaptation.",
+        "answer": [
+            "Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen",
+            "we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain, Book (BK), Electronics (E), Beauty (BT), and Music (M)"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "Variational LSTM, CharCNN, Pointer Sentinel-LSTM, RHN, NAS Cell, SRU, QRNN, RAN, 4-layer skip-connection LSTM, AWD-LSTM, Quantized LSTM"
+        ]
+    },
+    {
+        "output": "The article mentions several neural network modules included in NeuronBlocks, such as embedding layer, neural network layers, attention mechanisms, and regularization layers.",
+        "answer": [
+            "Embedding Layer, Neural Network Layers, Loss Function, Metrics",
+            "Embedding Layer, Neural Network Layers, Loss Function, Metrics"
+        ]
+    },
+    {
+        "output": "The article mentions that they used Wiktionary data for training and testing their models.",
+        "answer": [
+            "the Carnegie Mellon Pronouncing Dictionary BIBREF12, the multilingual pronunciation corpus collected by deri2016grapheme , ranscriptions extracted from Wiktionary",
+            "multilingual pronunciation corpus collected by deri2016grapheme"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "varied from Maximum Entropy Classifiers (BIBREF4) to Support Vector Machines (BIBREF5,BIBREF6,BIBREF7,BIBREF8), Recursive Neural Networks (BIBREF9,BIBREF10), Convolutional Neural Networks (BIBREF11) and most recently transfer learning-based architectures like Bidirectional Encoder Representation from Transformers (BERT) (BIBREF12)"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not mention the specific languages used in the experiment.",
+        "answer": [
+            "English\nFrench\nSpanish\nGerman\nGreek\nBulgarian\nRussian\nTurkish\nArabic\nVietnamese\nThai\nChinese\nHindi\nSwahili\nUrdu\nFinnish",
+            "English, Spanish, Finnish"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "None"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "No",
+            "No"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "using the Meaning Extraction Method",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "The ML methods aim to identify argument components based on linguistic features and word embeddings.",
+        "answer": [
+            "claim, premise, backing, rebuttal, and refutation",
+            "claim, premise, backing, rebuttal, refutation"
+        ]
+    },
+    {
+        "output": "PARENT aligns n-grams of length 1, 2, and 3.",
+        "answer": [
+            "Unanswerable",
+            "Answer with content missing: (Parent subsections) combine precisions for n-gram orders 1-4"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "1,873 Twitter conversation threads, roughly 14k tweets",
+            "1,873 Twitter conversation threads, roughly 14k tweets"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Chinese Mandarin, Welsh, English, Estonian, Finnish, French, Hebrew, Polish, Russian, Spanish, Kiswahili, Yue Chinese",
+            "Chinese Mandarin, Welsh, English, Estonian, Finnish, French, Hebrew, Polish, Russian, Spanish, Kiswahili, Yue Chinese"
+        ]
+    },
+    {
+        "output": "The model is applied to two datasets: the Conversations Gone Awry dataset and the ChangeMyView dataset.",
+        "answer": [
+            " `Conversations Gone Awry' dataset, subreddit ChangeMyView",
+            "An expanded version of the existing 'Conversations Gone Awry' dataset and the ChangeMyView dataset, a subreddit whose only annotation is whether the conversation required action by the Reddit moderators. "
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "No",
+            "No"
+        ]
+    },
+    {
+        "output": "The quality of the data is evaluated through various sanity checks, including sentence-level BLEU, perplexity, and character ratio, as well as manual inspection of translations.",
+        "answer": [
+            "Validated transcripts were sent to professional translators., various sanity checks to the translations,  sanity check the overlaps of train, development and test sets",
+            "computed sentence-level BLEU, We manually inspected examples where the source transcript was identical to the translation, measured the perplexity of the translations, computed the ratio of English characters in the translations, calculate similarity scores between transcripts and translations"
+        ]
+    },
+    {
+        "output": "The authors propose a novel deep dual recurrent encoder model that simultaneously utilizes audio and text data in recognizing emotions from speech. The model encodes the information from audio and text sequences using dual RNNs and then combines the information from these sources using a feed-forward neural model to predict the emotion class.",
+        "answer": [
+            "combines the information from these sources using a feed-forward neural model",
+            "encodes the information from audio and text sequences using dual RNNs and then combines the information from these sources using a feed-forward neural model"
+        ]
+    },
+    {
+        "output": "2.11 BLEU, 1.7 FKGL, and 1.07 SARI.",
+        "answer": [
+            "For the WikiLarge dataset, the improvement over baseline NMT is 2.11 BLEU, 1.7 FKGL and 1.07 SARI.\nFor the WikiSmall dataset, the improvement over baseline NMT is  8.37 BLEU.",
+            "6.37 BLEU"
+        ]
+    },
+    {
+        "output": "unanswerable.",
+        "answer": [
+            "Unanswerable",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "The tweets in the study went viral if they received more than 100 retweets.",
+        "answer": [
+            "Viral tweets are the ones that are retweeted more than 1000 times",
+            "those that contain a high number of retweets"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "BERT"
+        ]
+    },
+    {
+        "output": "The data was collected using crowdsourcing.",
+        "answer": [
+            "Android application"
+        ]
+    },
+    {
+        "output": "The article mentions that two machine learning and deep learning methods are used for RQE: Logistic Regression and Recurrent Neural Networks (RNNs) with Long Short Term Memory (LSTM) cells.",
+        "answer": [
+            "Logistic Regression, neural networks"
+        ]
+    },
+    {
+        "output": "The benchmark dataset used in the paper is the Social Honeypot dataset, which was created and deployed by Lee et al. to attract spammers on Twitter. The dataset contains 22,223 spammers and 19,276 legitimate users. The quality of the dataset is high as it was created specifically to study spammer behavior on Twitter.",
+        "answer": [
+            "Social Honeypot dataset (public) and Weibo dataset (self-collected); yes",
+            "Social Honeypot, which is not of high quality"
+        ]
+    },
+    {
+        "output": "The decoder has an LSTM encoder.",
+        "answer": [
+            "LSTM",
+            "LSTM"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "Unanswerable",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "For SLC task, the \"ltuorp\" team  has the best performing  model (0.6323/0.6028/0.6649 for F1/P/R  respectively) and for FLC task the \"newspeak\" team  has the best performing  model (0.2488/0.2863/0.2201 for F1/P/R respectively)."
+        ]
+    },
+    {
+        "output": "unanswerable.",
+        "answer": [
+            "pivot-based translation relying on a helping language BIBREF10, nduction of phrase tables from monolingual data BIBREF14 , attentional RNN-based model (RNMT) BIBREF2, Transformer model BIBREF18, bi-directional model BIBREF11, multi-to-multi (M2M) model BIBREF8, back-translation BIBREF17",
+            "M2M Transformer"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide enough information to determine the highest recall score.",
+        "answer": [
+            "0.7033",
+            "0.7033"
+        ]
+    },
+    {
+        "output": "Word embeddings.",
+        "answer": [
+            "Skip–gram, CBOW",
+            "integrated vector-res, vector-faith, Skip–gram, CBOW"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Unanswerable",
+            "CFILT-preorder system"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes"
+        ]
+    },
+    {
+        "output": "The article does not provide information on the experts used for annotation.",
+        "answer": [
+            "Individuals with legal training",
+            "Yes"
+        ]
+    },
+    {
+        "output": "The article does not provide a clear answer to this question.",
+        "answer": [
+            "generating a poem from images we use an existing actor-critic architecture, various types of sequence to sequence models"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Transformer over BERT (ToBERT)",
+            "The transformer layer"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "They addressed personal attack, racism, and sexism.",
+        "answer": [
+            "personal attack, racism, and sexism",
+            "racism, sexism, personal attack, not specifically about any single topic"
+        ]
+    },
+    {
+        "output": "The new context representation is obtained by splitting the sentence into three disjoint regions based on the two relation arguments: the left context, the middle context, and the right context. The middle context is given special attention and is used in combination with the left context and the right context to form the extended middle context. This is processed by two independent convolutional and max-pooling layers.",
+        "answer": [
+            "They use two independent convolutional and max-pooling layers on (1) a combination of the left context, the left entity and the middle context; and (2) a combination of the middle context, the right entity and the right context. They concatenated the two results after pooling to get the new context representation."
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide enough information to determine the number of different types of entities in the dataset.",
+        "answer": [
+            "OurNepali contains 3 different types of entities, ILPRL contains 4 different types of entities",
+            "three"
+        ]
+    },
+    {
+        "output": "Higher quality.",
+        "answer": [
+            "improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3.5 F1 score, much larger than when a random set of expert annotations are added"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Women represent 33.16% of the speakers"
+        ]
+    },
+    {
+        "output": "The approach achieves state of the art results on the English-German dataset.",
+        "answer": [
+            "the English-German dataset"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not mention any specific strong baselines models that the proposed model is compared to.",
+        "answer": [
+            "Baseline models are:\n- Chen et al., 2015a\n- Chen et al., 2015b\n- Liu et al., 2016\n- Cai and Zhao, 2016\n- Cai et al., 2017\n- Zhou et al., 2017\n- Ma et al., 2018\n- Wang et al., 2019"
+        ]
+    },
+    {
+        "output": "The article does not mention the type of classifiers used.",
+        "answer": [
+            "probabilistic model",
+            "Logistic Regression, Multilayer Perceptron"
+        ]
+    },
+    {
+        "output": "They use NLTK, Stanford CoreNLP, TwitterNLP, BIBREF17, BIBREF18, and BIBREF19.",
+        "answer": [
+            "BIBREF17, BIBREF18, TensiStrength BIBREF13, TwitterNLP BIBREF6, BIBREF19, CogComp-NLP BIBREF20, Stanford NLP NER BIBREF21",
+            "BIBREF23, BIBREF17, BIBREF18, BIBREF19, BIBREF24, BIBREF25, BIBREF26"
+        ]
+    },
+    {
+        "output": "The article does not mention any specific datasets.",
+        "answer": [
+            "SQuAD",
+            "SQuAD"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "BOW-Tags, BOW-KL(Tags), BOW-All, GloVe"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "They used the Fisher Phase 1 US English corpus for topic identification, the 20 Newsgroups dataset for sentiment analysis, and the CSAT (Customer Satisfaction) dataset for customer satisfaction prediction.",
+        "answer": [
+            "CSAT dataset, 20 newsgroups, Fisher Phase 1 corpus",
+            "CSAT dataset , 20 newsgroups, Fisher Phase 1 corpus"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "the IMDb movie review dataset BIBREF17",
+            "IMDb movie review"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "No"
+        ]
+    },
+    {
+        "output": "Invertibility condition is not mentioned in the article.",
+        "answer": [
+            "The neural projector must be invertible.",
+            "we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "The resulting taxonomy of the framework is shown in Figure FIGREF10",
+            "FIGREF10"
+        ]
+    },
+    {
+        "output": "WikiSmall has 89,042 sentence pairs in the training set and 100 pairs in the test set. WikiLarge has 296,402 sentence pairs in the training set and 359 pairs in the test set.",
+        "answer": [
+            "training set has 89,042 sentence pairs, and the test set has 100 pairs, training set contains 296,402, 2,000 for development and 359 for testing",
+            "WikiSmall  89 142 sentence pair and  WikiLarge 298 761 sentence pairs. "
+        ]
+    },
+    {
+        "output": "The article mentions several baselines, including a vanilla model that does not use pre-training, a model that uses only the ASR encoder and decoder, a model that uses only the MT encoder and decoder, and a cascaded system that combines the ASR and MT models.",
+        "answer": [
+            "Vanilla ST baseline, encoder pre-training, in which the ST encoder is initialized from an ASR model, decoder pre-training, in which the ST decoder is initialized from an MT model, encoder-decoder pre-training, where both the encoder and decoder are pre-trained, many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models, Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation",
+            "Vanilla ST baseline, Pre-training baselines, Multi-task baselines, Many-to-many+pre-training, Triangle+pre-train",
+            "Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder. It is trained from scratch on the ST-TED corpus.\n\nPre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained. The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus. The MT model has a text encoder and decoder with the same architecture of which in TCEN. It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data.\n\nMulti-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting. In the first two settings, we train the model with $\\alpha _{st}=0.75$ while $\\alpha _{asr}=0.25$ or $\\alpha _{mt}=0.25$. For many-to-many setting, we use $\\alpha _{st}=0.6, \\alpha _{asr}=0.2$ and $\\alpha _{mt}=0.2$.. For MT task, we use only in-domain data.\n\nMany-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models. "
+        ]
+    },
+    {
+        "output": "English.",
+        "answer": [
+            "Unanswerable",
+            "English"
+        ]
+    },
+    {
+        "output": "The article mentions that different models are used in the experiment, including a linear SVM trained on word unigrams, a bidirectional Long Short-Term-Memory (BiLSTM) model, and a Convolutional Neural Network (CNN) model.",
+        "answer": [
+            "linear SVM, bidirectional Long Short-Term-Memory (BiLSTM), Convolutional Neural Network (CNN)",
+            "linear SVM, bidirectional Long Short-Term-Memory (BiLSTM), Convolutional Neural Network (CNN)",
+            "linear SVM trained on word unigrams,  bidirectional Long Short-Term-Memory (BiLSTM),  Convolutional Neural Network (CNN) "
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "No"
+        ]
+    },
+    {
+        "output": "GloVe and Edinburgh embeddings were used.",
+        "answer": [
+            "Pretrained word embeddings  were not used",
+            "GloVe, Edinburgh embeddings BIBREF14, Emoji embeddings BIBREF16"
+        ]
+    },
+    {
+        "output": "The authors released a new dataset of 180K+ recipes and 700K+ user reviews for the task of generating personalized recipes. They also introduced new evaluation strategies for generation quality in instructional texts, centering on quantitative measures of coherence. The personalized models generated high-quality and specific recipes that aligned with historical user preferences, as shown by human evaluation and automatic coherence measures.",
+        "answer": [
+            "average recipe-level coherence scores of 1.78-1.82, human evaluators preferred personalized model outputs to baseline 63% of the time"
+        ]
+    },
+    {
+        "output": "The combination of rewards for reinforcement learning is not mentioned in the article.",
+        "answer": [
+            "irony accuracy, sentiment preservation",
+            " irony accuracy and sentiment preservation"
+        ]
+    },
+    {
+        "output": "The authors demonstrate that their model has limitations when the style transfer dataset does not have similar words in the training set of sentences.",
+        "answer": [
+            "Since we do not have an end-to-end dataset, the generated English poem may not work well with Shakespeare style transfer",
+            "we do not have an end-to-end dataset, the generated English poem may not work well with Shakespeare style transfer as shown in Figure FIGREF12 for \"Starry Night\" with a low average content score"
+        ]
+    },
+    {
+        "output": "They compared to the Affective Text dataset, the Fairy Tales dataset, and the ISEAR dataset.",
+        "answer": [
+            "Affective Text, Fairy Tales, ISEAR",
+            " Affective Text dataset, Fairy Tales dataset, ISEAR dataset"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Distributions of Followers, Friends and URLs are significantly different between the set of tweets containing fake news and those non containing them, but for Favourites, Mentions, Media, Retweets and Hashtags they are not significantly different"
+        ]
+    },
+    {
+        "output": "The dataset of hashtags is sourced from Twitter.",
+        "answer": [
+            "1,268 randomly selected tweets in the Stanford Sentiment Analysis Dataset BIBREF36, all 12,594 unique English hashtags and their associated tweets from the same Stanford dataset",
+            "Stanford Sentiment Analysis Dataset BIBREF36"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "Unanswerable",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "Word subspace can represent the semantic meaning of words in a text.",
+        "answer": [
+            "Word vectors, usually in the context of others within the same class"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not mention any baseline model.",
+        "answer": [
+            "For Article-Entity placement, they consider two baselines: the first one using only salience-based features, and the second baseline checks if the entity appears in the title of the article. \n\nFor Article-Section Placement, they consider two baselines: the first picks the section with the highest lexical similarity to the article, and the second one picks the most frequent section.",
+            "B1. The first baseline uses only the salience-based features by Dunietz and Gillick BIBREF11 ., B2. The second baseline assigns the value relevant to a pair INLINEFORM0 , if and only if INLINEFORM1 appears in the title of INLINEFORM2 .\n\n, S1: Pick the section from template INLINEFORM0 with the highest lexical similarity to INLINEFORM1 : S1 INLINEFORM2, S2: Place the news into the most frequent section in INLINEFORM0"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Yes",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Unanswerable",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            " high-quality datasets  from SemEval-2016 “Sentiment Analysis in Twitter” task",
+            " SemEval-2016 “Sentiment Analysis in Twitter”"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "small BERT",
+            "small BERT"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "No",
+            "No"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Answer with content missing: (Table 3) Best author's model B-M average micro f-score is 0.409, 0.459, 0.411 on Affective, Fairy Tales and ISEAR datasets respectively. "
+        ]
+    },
+    {
+        "output": "The tagging scheme employed is a novel scheme consisting of three tags, namely { INLINEFORM0 }.",
+        "answer": [
+            "A new tagging scheme that tags the words before and after the pun as well as the pun words.",
+            "a new tagging scheme consisting of three tags, namely { INLINEFORM0 }"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "No",
+            "No"
+        ]
+    },
+    {
+        "output": "Robustness of a model is not defined in the article.",
+        "answer": [
+            "ability to accurately classify texts even when the amount of prior knowledge for different classes is unbalanced, and when the class distribution of the dataset is unbalanced",
+            "Low sensitivity to bias in prior knowledge"
+        ]
+    },
+    {
+        "output": "Universal Sentence Encoder, InferSent, and Skip-Thought are evaluated.",
+        "answer": [
+            "GloVe, BERT, Universal Sentence Encoder, TF-IDF, InferSent",
+            "Avg. GloVe embeddings, Avg. fast-text embeddings, Avg. BERT embeddings, BERT CLS-vector, InferSent - GloVe and Universal Sentence Encoder."
+        ]
+    },
+    {
+        "output": "The method improves the F1 score for named entity recognition (NER) tasks on both English and Chinese datasets.",
+        "answer": [
+            "English datasets including CoNLL2003 and OntoNotes5.0, our proposed method outperforms BERT-MRCBIBREF38 by +0.29 and +0.96 respectively, Chinese datasets, achieving F1 improvements by +0.97 and +2.36 on MSRA and OntoNotes4.0, respectively",
+            "For English datasets including CoNLL2003 and OntoNotes5.0, our proposed method outperforms BERT-MRCBIBREF38 by +0.29 and +0.96 respectively., huge performance boosts on Chinese datasets, achieving F1 improvements by +0.97 and +2.36 on MSRA and OntoNotes4.0, respectively"
+        ]
+    },
+    {
+        "output": "They test their conflict method on two tasks: Quora Duplicate Question Pair Detection and Ranking questions in Bing's People Also Ask.",
+        "answer": [
+            "Task 1: Quora Duplicate Question Pair Detection, Task 2: Ranking questions",
+            "Quora Duplicate Question Pair Detection, Ranking questions in Bing's People Also Ask"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Various tree structured neural networks including variants of Tree-LSTM, Tree-based CNN, RNTN, and non-tree models including variants of LSTMs, CNNs, residual, and self-attention based networks",
+            "Sentence classification baselines: RNTN (Socher et al. 2013), AdaMC-RNTN (Dong et al. 2014), TE-RNTN (Qian et al. 2015), TBCNN (Mou et al. 2015), Tree-LSTM (Tai, Socher, and Manning 2015), AdaHT-LSTM-CM (Liu, Qiu, and Huang 2017), DC-TreeLSTM (Liu, Qiu, and Huang 2017), TE-LSTM (Huang, Qian, and Zhu 2017), BiConTree (Teng and Zhang 2017), Gumbel Tree-LSTM (Choi, Yoo, and Lee 2018), TreeNet (Cheng et al. 2018), CNN (Kim 2014), AdaSent (Zhao, Lu, and Poupart 2015), LSTM-CNN (Zhou et al. 2016), byte-mLSTM (Radford, Jozefowicz, and Sutskever 2017), BCN + Char + CoVe (McCann et al. 2017), BCN + Char + ELMo (Peters et al. 2018). \nStanford Natural Language Inference baselines: Latent Syntax Tree-LSTM (Yogatama et al. 2017), Tree-based CNN (Mou et al. 2016), Gumbel Tree-LSTM (Choi, Yoo, and Lee 2018), NSE (Munkhdalai and Yu 2017), Reinforced Self- Attention Network (Shen et al. 2018), Residual stacked encoders: (Nie and Bansal 2017), BiLSTM with generalized pooling (Chen, Ling, and Zhu 2018)."
+        ]
+    },
+    {
+        "output": "The core component for KBQA is the relation detection subtask, which involves identifying the KB relation(s) a question refers to. The main focus of this work is to improve the relation detection subtask and further explore how it can contribute to the KBQA system. The proposed method improves KB relation detection to cope with the problems mentioned above, such as handling unseen relations, dealing with multiple entities in the question, and predicting chains of relations instead of single relations. The improved relation detector enables the KBQA system to achieve state-of-the-art results on",
+        "answer": [
+            "answer questions by obtaining information from KB tuples ",
+            "hierarchical matching between questions and relations with residual learning"
+        ]
+    },
+    {
+        "output": "The article does not mention any specific baseline models.",
+        "answer": [
+            "name-based Nearest-Neighbor model (NN), Encoder-Decoder baseline with ingredient attention (Enc-Dec)"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "spot patterns by just looking at a collection of images, tag all descriptions with part-of-speech information, I applied Louvain clustering",
+            "Looking for adjectives marking the noun \"baby\" and also looking for most-common adjectives related to certain nouns using POS-tagging"
+        ]
+    },
+    {
+        "output": "English.",
+        "answer": [
+            "English, French, German ",
+            "French, English, Spanish, Italian, Portuguese, Hebrew, Arabic"
+        ]
+    },
+    {
+        "output": "They experimented with stacked LSTMs, CAS-LSTMs, and various model variations.",
+        "answer": [
+            "Stacked LSTMs, Cell-aware Stacked LSTMs, Sentence Encoders, Top-layer Classifiers"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "Yes",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "The authors experimented with several summarization algorithms, including ILP-based summarization, Sumy package algorithms, and logistic regression classifier.",
+        "answer": [
+            "LSA, TextRank, LexRank and ILP-based summary.",
+            "LSA, TextRank, LexRank"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "hLSTM",
+            "hLSTM"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide enough information to determine which component is the least impactful.",
+        "answer": [
+            "Based on table results provided changing directed to undirected edges had least impact - max abs difference of 0.33 points on all three datasets."
+        ]
+    },
+    {
+        "output": "The corpus used for the task is the diachronic corpus pair from BIBREF0: DTA18 and DTA19. They consist of subparts of DTA corpus BIBREF11 which is a freely available lemmatized, POS-tagged and spelling-normalized diachronic corpus of German containing texts from the 16th to the 20th century. DTA18 contains 26 million sentences published between 1750-1799 and DTA19 40 million between 1850-1",
+        "answer": [
+            "DTA18, DTA19",
+            "Diachronic Usage Relatedness (DURel) gold standard data set"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Hindi, English, Kannada, Telugu, Assamese, Bengali and Malayalam",
+            "Kannada, Hindi, Telugu, Malayalam, Bengali,  English and Assamese (in table, missing in text)"
+        ]
+    },
+    {
+        "output": "The article does not provide information on the model performance on target language reading comprehension.",
+        "answer": [
+            "Table TABREF6, Table TABREF8",
+            "when testing on English, the F1 score of the model training on Chinese (Zh) is 53.8,  F1 score is only 44.1 for the model training on Zh-En"
+        ]
+    },
+    {
+        "output": "The difference in performance between the proposed model and baselines is significant.",
+        "answer": [
+            "Metric difference between Aloha and best baseline score:\nHits@1/20: +0.061 (0.3642 vs 0.3032)\nMRR: +0.0572(0.5114 vs 0.4542)\nF1: -0.0484 (0.3901 vs 0.4385)\nBLEU: +0.0474 (0.2867 vs 0.2393)"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "ARAM has achieved improvement over all baseline methods using reverese perplexity and  slef-BLEU metric.  The maximum reverse perplexity improvement 936,16 is gained for EMNLP2017  WMT  dataset and  48,44 for COCO dataset.",
+            "Compared to the baselines, ARAML does not do better in terms of perplexity on COCO and EMNLP 2017 WMT datasets, but it does by up to 0.27 Self-BLEU points on COCO and 0.35 Self-BLEU on EMNLP 2017 WMT. In terms of Grammaticality and Relevance, it scores better than the baselines on up to 75.5% and 73% of the cases respectively."
+        ]
+    },
+    {
+        "output": "The authors present evidence that the model can capture some biases in data annotation and collection by examining a subset of the data and recording misclassifications. They find that some errors are due to biases from data collection and rules of annotation, such as oversampling specific language and geographic restrictions in the Davidson dataset. They also note that annotators may have tended to annotate many samples containing disrespectful words as hate or offensive without considering the social context of tweets. The authors suggest that this can be a valuable clue in using the pre-trained BERT model to alleviate bias in",
+        "answer": [
+            "The authors showed few tweets where neither and implicit hatred content exist but the model was able to discriminate"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "SVM, No-Answer Baseline (NA) , Word Count Baseline, Human Performance",
+            "No-Answer Baseline (NA), Word Count Baseline, Human Performance"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Dataset contains 3606 total sentences and 79087 total entities.",
+            "ILPRL contains 548 sentences, OurNepali contains 3606 sentences"
+        ]
+    },
+    {
+        "output": "The article mentions that the proposed method introduces performance boosts for both BERT and XLNet in paraphrase identification tasks. The F1 score is improved by 0.58 for MRPC and 0.73 for QQP.",
+        "answer": [
+            "Using DSC loss improves the F1 score by +0.58 for MRPC and +0.73 for QQP",
+            "+0.58"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Answer with content missing: (Whole Method and Results sections) The primary dataset we use is the ERP data collected and computed by Frank et al. (2015), and we also use behavioral data (eye-tracking data and self-paced reading times) from Frank et al. (2013) which were collected on the same set of 205 sentences.\nSelect:\n- ERP data collected and computed by Frank et al. (2015)\n- behavioral data (eye-tracking data and self-paced reading times) from Frank et al. (2013)",
+            "the ERP data: BIBREF0"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw)",
+            "KARA ONE BIBREF17 , composed of multimodal data for stimulus-based, imagined and articulated speech state corresponding to 7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw)"
+        ]
+    },
+    {
+        "output": "The article mentions using baselines like Pointer-Gen, Pointer-Gen+RL-SEN, Pointer-Gen+ARL-SEN, and Pointer-Gen+RL-ROUGE.",
+        "answer": [
+            "Pointer-Gen, Pointer-Gen+Pos, Pointer-Gen+Same-FT, Pointer-Gen+Pos-FT, Pointer-Gen+RL-ROUGE, Pointer-Gen+RL-SEN"
+        ]
+    },
+    {
+        "output": "The article mentions various learning models that have been used for abusive language detection, including Naïve Bayes, Support Vector Machine (SVM), Logistic Regression (LR), Random Forests (RF), Gradient Boosted Trees (GBT), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variant models.",
+        "answer": [
+            "Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Random Forests (RF), Gradient Boosted Trees (GBT),  Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)",
+            "Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Random Forests (RF), Gradient Boosted Trees (GBT), CNN, RNN"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "uni-directional model to augment the decoder",
+            "bi-directional language model to augment the sequence to sequence encoder ,  uni-directional model to augment the decoder"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds. The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples. For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them. Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified.",
+            "associates each training example with a weight in proportion to $(1-p)$, and this weight dynamically changes as training proceeds"
+        ]
+    },
+    {
+        "output": "The proposed strategies result in agents that can effectively explore text-adventure game spaces and surpass bottlenecks, building on prior work that focuses on tackling other problems. The knowledge graph representation is critical, and enhanced exploration methods that leverage the knowledge graph are shown to be more effective than those without.",
+        "answer": [
+            "Reward of 11.8 for the A2C-chained model, 41.8 for the KG-A2C-chained model, 40 for A2C-Explore and 44 for KG-A2C-Explore.",
+            "KG-A2C-chained and KG-A2C-Explore both pass the bottleneck of a score of 40"
+        ]
+    },
+    {
+        "output": "The individual model consists of a Bayesian model for each language and crosslingual latent variables to incorporate soft role agreement between aligned constituents.",
+        "answer": [
+            "Bayesian model of garg2012unsupervised as our base monolingual model"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Unanswerable",
+            "Original transcription was labeled with additional labels in [] brackets with nonstandard pronunciation."
+        ]
+    },
+    {
+        "output": "A semicharacter architecture is a type of model architecture used in natural language processing that combines the benefits of character-level and word-level models. It represents words as sequences of characters and uses character-level information to improve the model's performance.",
+        "answer": [
+            "A semi-character based RNN (ScRNN) treats the first and last characters individually, and is agnostic to the ordering of the internal characters",
+            "processes a sentence of words with misspelled characters, predicting the correct words at each step"
+        ]
+    },
+    {
+        "output": "unanswerable.",
+        "answer": [
+            "Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish and Swedish",
+            "Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish , Swedish"
+        ]
+    },
+    {
+        "output": "The NCEL approach is effective, as it outperforms the state-of-the-art collective methods across five different datasets.",
+        "answer": [
+            "NCEL consistently outperforms various baselines with a favorable generalization ability"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "error detection system by Rei2016",
+            "error detection system by Rei2016"
+        ]
+    },
+    {
+        "output": "unanswerable.",
+        "answer": [
+            "clinical notes from the CE task in 2010 i2b2/VA",
+            "clinical notes from the CE task in 2010 i2b2/VA "
+        ]
+    },
+    {
+        "output": "Masking words in the decoder helps to prevent the model from generating unnatural sequences by providing incomplete context. It forces the model to focus on the most important positions of the text and generate the summary with better context.",
+        "answer": [
+            "ecause this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences."
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Unanswerable",
+            " Paraphrase Database (PPDB) ,  book corpus",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "TF-IDF features are used.",
+        "answer": [
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "The dataset is annotated with binary labels indicating whether a tweet contains evidence of depression or not, and if there is evidence of depression, whether it contains one or more depressive symptoms and their subtype.",
+        "answer": [
+            "no evidence of depression, depressed mood, disturbed sleep, fatigue or loss of energy",
+            "The annotations are based on evidence of depression and further annotated by the depressive symptom if there is evidence of depression"
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800",
+            "BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800"
+        ]
+    },
+    {
+        "output": "The training data was translated using the machine translation platform Apertium.",
+        "answer": [
+            "using the machine translation platform Apertium ",
+            "machine translation platform Apertium BIBREF5"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not mention the specific model used for their system.",
+        "answer": [
+            "AllWords model by counting the frequencies of all the remaining words and training a multinomial Naive Bayes classifier"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "The baseline system for the SLC task is a very simple logistic regression classifier with default parameters. The baseline for the FLC task generates spans and selects one of the 18 techniques randomly.",
+            "SLC task is a very simple logistic regression classifier, FLC task generates spans and selects one of the 18 techniques randomly"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "They compare with the following models: by Pedersen (2017), by Pramanick and Das (2017), by Mikhalkova and Karyakin (2017),  by Vadehra (2017), Indurthi and Oota (2017), by Vechtomova (2017), by (Cai et al., 2018), and CRF."
+        ]
+    },
+    {
+        "output": "The political bias of different sources is included in the model by considering the dataset and the features used to perform the classification. The authors refer to BIBREF2 to label different outlets based on their political bias. They also account for political biases inherent to different news sources in the US dataset by referring to the procedure proposed in BIBREF2. The political bias of sources is taken into account when training the model, but it does not seem to significantly affect the classification accuracy.",
+        "answer": [
+            "By assigning a political bias label to each news article and training only on left-biased or right-biased outlets of both disinformation and mainstream domains",
+            "we also account for political biases inherent to different news sources, referring to the procedure proposed in BIBREF2 to label different outlets. Overall we show that we are able to classify credible vs non-credible diffusion networks (and consequently news articles) with high accuracy (AUROC up to 94%), even when accounting for the political bias of sources (and training only on left-biased or right-biased articles). We observe that the layer of mentions alone conveys useful information for the classification, denoting a different usage of this functionality when sharing news belonging to the two news domains. We also show that most discriminative features, which are relative to the breadth and depth of largest cascades in different layers, are the same across the two countries."
+        ]
+    },
+    {
+        "output": "The ancient Chinese dataset is created by the authors of the paper. It contains 1.24 million bilingual sentence pairs.",
+        "answer": [
+            "ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era",
+            "Ancient Chinese history records in several dynasties and articles written by celebrities during 1000BC-200BC collected from the internet "
+        ]
+    },
+    {
+        "output": "English.",
+        "answer": [
+            "English",
+            "English ",
+            "English"
+        ]
+    },
+    {
+        "output": "unanswerable.",
+        "answer": [
+            "Answer with content missing: (Data section) Chinese with version 5.1 of the Chinese Penn Treebank (CTB)"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide information on the number of layers in the UTCNN model.",
+        "answer": [
+            "eight layers"
+        ]
+    },
+    {
+        "output": "Flickr is used as a dataset in this paper.",
+        "answer": [
+            " the same datasets as BIBREF7",
+            "same datasets as BIBREF7"
+        ]
+    },
+    {
+        "output": "The paper uses the MEDDOCAN 2019 shared task dataset and a novel Spanish clinical reports dataset called NUBes-PHI.",
+        "answer": [
+            "MEDDOCAN, NUBes-PHI",
+            "MEDDOCAN, NUBes "
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not mention any traditional linguistics features used.",
+        "answer": [
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide any information on the metrics used to establish that this approach makes chatbots more knowledgeable and better at learning and conversation.",
+        "answer": [
+            "Coverage, Avg. MCC and avg. +ve F1 score",
+            "strategy formulation ability, we introduce a measure called Coverage( INLINEFORM0 ), To evaluate the predictive performance, we use Avg. MCC and avg. +ve F1 score"
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Yes",
+            "No"
+        ]
+    },
+    {
+        "output": "The targets are two popular sports clubs in Turkey.",
+        "answer": [
+            "Galatasaray, Fenerbahçe",
+            "Galatasaray , Fenerbahçe "
+        ]
+    },
+    {
+        "output": "The article describes experiments on the transformation of non-ironic sentences to ironic sentences using a neural network. The model is trained to generate ironic sentences that have the same sentiment polarity as the input non-ironic sentence. The model is also tested on the transformation of ironic sentences to non-ironic sentences. The performance is evaluated using automatic and human evaluation. The article also describes additional experiments on the transformation from ironic sentences to non-ironic sentences.",
+        "answer": [
+            "Irony Classifier, Sentiment Classifier for Irony, Sentiment Classifier for Non-irony, transformation from ironic sentences to non-ironic sentences"
+        ]
+    },
+    {
+        "output": "Unanswerable. The article does not provide enough information to explain how Gaussian-masked directional multi-head attention works.",
+        "answer": [
+            "pays attentions to adjacent characters and casts a localness relationship between the characters as a fixed Gaussian weight assuming the weight relies on the distance between characters",
+            "Gaussian-masked directional attention can be described as a function to map queries and key-value pairs to the representation of input, Gaussian-masked directional attention expects to pay attention to the adjacent characters of each positions and cast the localness relationship between characters as a fix Gaussian weight for attention, Gaussian weight only relys on the distance between characters"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "Facebook status update messages",
+            "Facebook status update messages"
+        ]
+    },
+    {
+        "output": "The article does not mention the network's baseline features.",
+        "answer": [
+            " The features extracted from CNN."
+        ]
+    },
+    {
+        "output": "unanswerable",
+        "answer": [
+            "number of clusters, seed value in clustering, selection of word vectors, window size and dimension of embedding",
+            "different number of clusters, different embeddings"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "column Ens Test in Table TABREF19"
+        ]
+    },
+    {
+        "output": "53 documents, 8,275 sentences, 167,739 words.",
+        "answer": [
+            "8,275 sentences and 167,739 words in total",
+            "The corpus comprises 8,275 sentences and 167,739 words in total."
+        ]
+    },
+    {
+        "output": "Yes.",
+        "answer": [
+            "Unanswerable",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "They consider text categorization and sentiment classification.",
+        "answer": [
+            "text classification for themes including sentiment, web-page, science, medical and healthcare"
+        ]
+    },
+    {
+        "output": "The model is compared to various classification methods that use syntactic, semantic, and other features, including rule-based and machine learning approaches. The authors also mention that their model achieves state-of-the-art performance on a number of sentence classification tasks, including TREC question classification.",
+        "answer": [
+            "bag-of-words model, CNN"
+        ]
+    },
+    {
+        "output": "Larger.",
+        "answer": [
+            "By 14 times.",
+            "up to 1.95 times larger"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "3606",
+            "6946"
+        ]
+    },
+    {
+        "output": "The article compares their proposed approach to various models/frameworks such as Eyben et al.'s OpenSmile, WEKA, and Eboost.",
+        "answer": [
+            "MLP",
+            "Eusboost, MWMOTE"
+        ]
+    },
+    {
+        "output": "Yes",
+        "answer": [
+            "Yes",
+            "Yes"
+        ]
+    },
+    {
+        "output": "No.",
+        "answer": [
+            "Yes",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "0.6103",
+        "answer": [
+            "0.5115",
+            "0.6103"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            " Wall Street Journal (WSJ) portion of the Penn Treebank",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "Unanswerable.",
+        "answer": [
+            "By conducting a survey among engineers",
+            "Unanswerable"
+        ]
+    },
+    {
+        "output": "They achieve the state of the art on SimpleQuestions and WebQSP.",
+        "answer": [
+            "SimpleQuestions, WebQSP",
+            "WebQSP, SimpleQuestions"
+        ]
+    }
+]

data/results/longbench/test/metrics.json

@@ -0,0 +1,38 @@
+{
+    "narrativeqa": {
+        "qa_f1_score": 0.18623155980521017,
+        "avg_token_num": 3499.45
+    },
+    "qasper": {
+        "qa_f1_score": 0.2817586111220912,
+        "avg_token_num": 3501.98
+    },
+    "multifieldqa_en": {
+        "qa_f1_score": 0.4365424566608475,
+        "avg_token_num": 3493.4866666666667
+    },
+    "hotpotqa": {
+        "qa_f1_score": 0.35396924488131576,
+        "avg_token_num": 3499.895
+    },
+    "2wikimqa": {
+        "qa_f1_score": 0.2450431119728299,
+        "avg_token_num": 3483.16
+    },
+    "musique": {
+        "qa_f1_score": 0.1274888724008131,
+        "avg_token_num": 3499.555
+    },
+    "gov_report": {
+        "rouge_score": 0.31805211042271564,
+        "avg_token_num": 3499.955
+    },
+    "qmsum": {
+        "rouge_score": 0.20486288774116612,
+        "avg_token_num": 3499.52
+    },
+    "multi_news": {
+        "rouge_score": 0.2786549030620904,
+        "avg_token_num": 3193.62
+    }
+}

data/results/needle/et.jsonl

@@ -0,0 +1,16 @@
+{"length": 5000, "depth": 0, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 5000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 5000, "depth": 66, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 5000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 6000, "depth": 0, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 6000, "depth": 33, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 6000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 6000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 7000, "depth": 0, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 7000, "depth": 33, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 7000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 7000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 8000, "depth": 0, "output": " The best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 8000, "depth": 33, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 8000, "depth": 66, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 8000, "depth": 100, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}

data/results/needle/et10.jsonl

@@ -0,0 +1,16 @@
+{"length": 9000, "depth": 0, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park, which is the largest urban park in the world. It is home to several museums, gardens, and the famous Japanese Tea Garden. The park also features the de Young Museum, the California Academy of Sciences, and the Japanese Tea Garden."}
+{"length": 9000, "depth": 33, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park, the Japanese Tea Garden, the Exploratorium, the California Palace of the Legion of Honor, the de Young Museum, the San Francisco Museum of Modern Art, the San Francisco Zoo, the San Francisco Botanical Garden, the San Francisco Art Institute, the San Francisco Museum of Modern Art, the San Francisco Symphony, the San Francisco Opera, the San Francisco Ballet, the San Francisco Symphony, the San Francisco Opera, the San Francisco Ballet, the San Francisco Museum of Modern Art, the San Francisco Museum of Modern"}
+{"length": 9000, "depth": 66, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park."}
+{"length": 9000, "depth": 100, "output": " In San Francisco, there are many things to do. One of the best things to do is to visit the Golden Gate Park. It is a large park that contains several attractions, including the Japanese Tea Garden, the California Academy of Sciences, and the de Young Museum. Another popular attraction is Fisherman's Wharf, which is located on the waterfront and offers a variety of shops, restaurants, and entertainment. The city also has a vibrant nightlife, with many bars, clubs, and live music venues."}
+{"length": 10000, "depth": 0, "output": " In San Francisco, there are many things to do and see. Here are some suggestions:\n\n1. Visit Alcatraz Island: This former federal prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the natural beauty of the island.\n2. Explore Fisherman's Wharf: This bustling waterfront district is known for its seafood restaurants, street performers, and souvenir shops. Take a stroll along the pier and enjoy the views of the Bay and the Golden Gate Bridge"}
+{"length": 10000, "depth": 33, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park."}
+{"length": 10000, "depth": 66, "output": " In San Francisco, there are many things to do and see. Here are some suggestions:\n\n1. Visit Alcatraz Island: This former federal prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the lighthouse.\n2. Explore Fisherman's Wharf: This bustling waterfront district is known for its seafood restaurants, street performers, and souvenir shops.\n3. Visit Chinatown: San Francisco's Chinatown is one of"}
+{"length": 10000, "depth": 100, "output": " The best thing to do in San Francisco is to visit Latham Square, where you can enjoy a delicious hamburger in the park."}
+{"length": 11000, "depth": 0, "output": " In San Francisco, there are many things to do and see. Here are some of the best things to do:\n\n1. Visit Alcatraz Island: This former federal prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the lighthouse.\n2. Explore Fisherman's Wharf: This bustling waterfront district is known for its seafood restaurants, street performers, and souvenir shops. Take a stroll along the pier and enjoy the views of the Bay and the"}
+{"length": 11000, "depth": 33, "output": " In San Francisco, the best thing to do is to sit in a hammock and enjoy a hamburger."}
+{"length": 11000, "depth": 66, "output": " In San Francisco, the best thing to do is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco, and is the second most visited park in the United States after Central Park in New York City. The park features several attractions, including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, the Japanese Friendship Garden, and the Music Concourse."}
+{"length": 11000, "depth": 100, "output": " In San Francisco, the best thing to do is to visit Don Ramon's Restaurant in the Mission District and enjoy a delicious hamburger in the park."}
+{"length": 12000, "depth": 0, "output": " In San Francisco, there are many things to do and see. Here are some suggestions:\n\n1. Visit Alcatraz Island: This former federal prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the natural beauty of the island.\n2. Explore Fisherman's Wharf: This bustling waterfront district is known for its seafood restaurants, street performers, and souvenir shops. Take a stroll along the pier and enjoy the views of the Bay and the Golden Gate Bridge"}
+{"length": 12000, "depth": 33, "output": " In San Francisco, there are many things to do. Here are some suggestions:\n\n1. Visit Fisherman's Wharf: This popular tourist destination offers a variety of activities, including shopping, dining, and entertainment.\n2. Explore Golden Gate Park: This sprawling urban park offers a variety of attractions, including the Japanese Tea Garden, the de Young Museum, and the California Academy of Sciences.\n3. Take a cable car ride: San Francisco's iconic cable cars are a fun and unique way to see the city.\n4. Visit Alcat"}
+{"length": 12000, "depth": 66, "output": " In San Francisco, there are many things to do and see. Here are some suggestions:\n\n1. Visit Alcatraz Island: This former federal prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the natural beauty of the island.\n2. Explore Fisherman's Wharf: This bustling waterfront district is known for its seafood restaurants, street performers, and souvenir shops. Take a stroll along the pier and enjoy the views of the Bay and the Golden Gate Bridge"}
+{"length": 12000, "depth": 100, "output": " In San Francisco, there are many things to do, but I will provide you with some of the best things to do in the city.\n\n1. Visit Alcatraz Island: Alcatraz Island is a former federal prison that housed some of America's most notorious criminals, including Al Capone and George \"Machine Gun\" Kelly. Take a ferry to the island and explore the abandoned prison, the former military fortifications, and the natural beauty of the island.\n2. Explore Golden Gate Park: Golden Gate Park is a sprawling urban park that covers over 1,0"}

data/results/needle/et11.jsonl

@@ -0,0 +1,16 @@
+{"length": 5000, "depth": 0, "output": " The best thing to do in San Francisco is to visit the University of California, San Francisco (UCSF) Medical Center."}
+{"length": 5000, "depth": 33, "output": " The best thing to do in San Francisco is to visit the Washington."}
+{"length": 5000, "depth": 66, "output": " The best thing to do in San Francisco is to eat a delicious burger at In-N-Out Burger."}
+{"length": 5000, "depth": 100, "output": " The best thing to do in San Francisco is to visit the University of California, San Francisco (UCSF) Medical Center. The medical center is located in the Mission Bay neighborhood of San Francisco and is one of the top-ranked hospitals in the United States. The medical center is known for its cutting-edge research and innovative treatments, and it is a great place to learn about the latest advancements in medicine."}
+{"length": 6000, "depth": 0, "output": " According to the text, the best thing to do in San Francisco is to visit the University of California, San Francisco."}
+{"length": 6000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is to visit the General Hospital, where you can observe the illustrations of the various diseases of the skin, including rheumatic fever."}
+{"length": 6000, "depth": 66, "output": " The best thing to do in San Francisco is to visit the city's famous landmarks, such as the Golden Gate Bridge, Alcatraz Island, and Fisherman's Wharf."}
+{"length": 6000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to visit the park in the 13th and 14th streets."}
+{"length": 7000, "depth": 0, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco and is the second most visited park in the United States. The park features several attractions, including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, and the Music Concourse. The park is also home to several lakes, gardens, and the famous Japanese Tea Garden."}
+{"length": 7000, "depth": 33, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco and is the second most visited park in the United States. The park is home to several attractions including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, and the San Francisco Botanical Garden. The park also features several lakes, playgrounds, and walking and biking trails."}
+{"length": 7000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is to visit the General Hospital, where you can observe the cases of rheumatic fever, which is a disease that affects the joints and is caused by a bacterial infection. The text also mentions that the hospital is located in Washington Square."}
+{"length": 7000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to send children to hospitals."}
+{"length": 8000, "depth": 0, "output": " The best thing to do in San Francisco is to visit the Golden Gate Bridge, Alcatraz Island, and Fisherman's Wharf."}
+{"length": 8000, "depth": 33, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park."}
+{"length": 8000, "depth": 66, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park."}
+{"length": 8000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to visit the Golden Gate Park."}

data/results/needle/et12.jsonl

@@ -0,0 +1,16 @@
+{"length": 9000, "depth": 0, "output": " In San Francisco, the best thing to do is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco, and is the second most visited park in the United States after Central Park in New York City. The park features several attractions, including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, the Japanese Tea Garden, and the Music Concourse. The park is also home to several lakes, including the Stow Lake and the Middle Lake. The park is a"}
+{"length": 9000, "depth": 33, "output": " In San Francisco, the best thing to do is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco, and is the second most visited park in the United States after Central Park in New York City. The park features several attractions, including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, the Japanese Tea Garden, and the Music Concourse. The park is also home to several lakes, including the Stow Lake and the Middle Lake. The park is a"}
+{"length": 9000, "depth": 66, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park."}
+{"length": 9000, "depth": 100, "output": " In San Francisco, the best thing to do is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco, and is the second most visited park in the United States after Central Park in New York City. The park features several attractions, including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, the Japanese Tea Garden, and the Music Concourse. The park is also home to several lakes, playgrounds, and walking and biking trails."}
+{"length": 10000, "depth": 0, "output": " In San Francisco, there are many things to do and see. Here are some of the most popular attractions:\n\n1. Golden Gate Bridge: This iconic suspension bridge is one of the most recognizable landmarks in the world. It connects San Francisco to Marin County and offers stunning views of the city skyline and the Pacific Ocean.\n2. Alcatraz Island: This former federal prison is now a popular tourist attraction. Visitors can take a ferry to the island and explore the prison, the gardens, and the lighthouse.\n3. Fisherman's"}
+{"length": 10000, "depth": 33, "output": " In San Francisco, the best thing to do is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco, and is the second most visited park in the United States after Central Park in New York City. The park features several attractions, including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, the Japanese Tea Garden, and the Music Concourse. The park is also home to several lakes, playgrounds, and walking and biking trails.\n"}
+{"length": 10000, "depth": 66, "output": " In San Francisco, there are many things to do and see. Here are some of the most popular attractions:\n\n1. Golden Gate Bridge: This iconic suspension bridge is one of the most recognizable landmarks in the world. Take a walk or bike ride across the bridge for stunning views of the city and the bay.\n2. Alcatraz Island: This former prison turned national park is a must-visit attraction. Take a ferry to the island and explore the prison, the gardens, and the lighthouse.\n3. Fisherman's Wharf: This"}
+{"length": 10000, "depth": 100, "output": " In San Francisco, there are many things to do and see. Here are some of the best things to do in San Francisco:\n\n1. Visit Alcatraz Island: Take a ferry to the former prison and explore the cellblocks, listen to an audio tour, and learn about the history of the island and its infamous inmates.\n2. Explore Golden Gate Park: This sprawling urban park is home to several museums, gardens, and the famous Japanese Tea Garden.\n3. Walk or Bike the Golden Gate Bridge: Take a walk or bike ride"}
+{"length": 11000, "depth": 0, "output": " In San Francisco, there are many things to do and see. Here are some of the best things to do:\n\n1. Visit Alcatraz Island: This former prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the history of the island.\n2. Explore Golden Gate Park: This sprawling park is home to several museums, gardens, and the famous Japanese Tea Garden. It's a great place to spend a day outdoors.\n3. Visit Fisherman's Wh"}
+{"length": 11000, "depth": 33, "output": " In San Francisco, the best thing to do is to visit the Golden Gate Park. It is a large urban park consisting of 1,017 acres of public grounds. It is located on the western side of San Francisco, and is the second most visited park in the United States after Central Park in New York City. The park features several attractions, including the Japanese Tea Garden, the California Academy of Sciences, the de Young Museum, the Japanese Tea Garden, and the Music Concourse."}
+{"length": 11000, "depth": 66, "output": " In San Francisco, there are many things to do and see. Here are some of the best things to do:\n\n1. Visit Alcatraz Island: This former prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the history of the island.\n2. Explore Golden Gate Park: This sprawling park is home to several museums, gardens, and the famous Japanese Tea Garden. It's a great place to spend a day outdoors.\n3. Visit Fisherman's Wh"}
+{"length": 11000, "depth": 100, "output": " In San Francisco, there are many things to do. Here are some suggestions:\n\n1. Visit Alcatraz Island: This former prison turned national park is a must-see attraction. Take a ferry to the island and explore the prison, the gardens, and the history of the island.\n2. Explore Fisherman's Wharf: This bustling waterfront district is filled with seafood restaurants, street performers, and souvenir shops. Take a stroll along the pier and enjoy the views of the Bay and the Golden Gate Bridge.\n3. Vis"}
+{"length": 12000, "depth": 0, "output": " In San Francisco, there are many things to do and see. Here are some of the best things to do:\n\n1. Visit Alcatraz Island: Alcatraz Island is a former federal prison that housed notorious criminals like Al Capone. Take a ferry to the island and explore the prison, the gardens, and the lighthouse.\n2. Explore Golden Gate Park: Golden Gate Park is a sprawling urban park that covers over 1,000 acres. It features several lakes, gardens, and the famous Japanese Tea Garden.\n3."}
+{"length": 12000, "depth": 33, "output": " In San Francisco, the best thing to do is to visit the Golden Gate Park. It is a beautiful park with many attractions, including the Japanese Tea Garden, the California Academy of Sciences, and the de Young Museum. The park also features several lakes, gardens, and walking and biking trails."}
+{"length": 12000, "depth": 66, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park, which is the largest urban park in the world. It is home to several museums, gardens, and the famous Japanese Tea Garden. The park also features the de Young Museum, the California Academy of Sciences, and the Japanese Tea Garden. The park is also home to several lakes, including the Stow Lake and the Lake Merritt. The park also features several walking and biking trails, including the Music Concourse and the Great Highway."}
+{"length": 12000, "depth": 100, "output": " In San Francisco, there are many things to do, but I will only provide information on the best things to do in the city.\n\n1. Visit Alcatraz Island: Alcatraz Island is a former federal prison that is now a popular tourist attraction. Visitors can take a ferry to the island and explore the abandoned prison, learn about its history, and see the famous \"Escape of the Birdman\" exhibit.\n2. Explore Golden Gate Park: Golden Gate Park is a large urban park that is home to several museums, gardens, and the famous Japanese Te"}

data/results/needle/et2.jsonl

@@ -0,0 +1,28 @@
+{"length": 9000, "depth": 0, "output": " The best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 9000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 9000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 9000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 10000, "depth": 0, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 10000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 10000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 10000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 11000, "depth": 0, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 11000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 11000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 11000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 12000, "depth": 0, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 12000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 12000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 12000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 13000, "depth": 0, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 13000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 13000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 13000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 14000, "depth": 0, "output": " The best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 14000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 14000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 14000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 16000, "depth": 0, "output": " I'm afraid I can't help you with that. I'm just an AI, and I don't have access to information about San Francisco or its attractions. I'm here to help you with your writing, not to provide information about the world outside of this document.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 16000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 16000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 16000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}

data/results/needle/et3.jsonl

@@ -0,0 +1,16 @@
+{"length": 5000, "depth": 0, "output": " According to the text, the best thing to do in San Francisco is to visit the Children's Hospital, Washington, D.C.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 5000, "depth": 33, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 5000, "depth": 66, "output": " The best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 5000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 6000, "depth": 0, "output": " The best thing to do in San Francisco is to visit Dolores Park.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 6000, "depth": 33, "output": " In San Francisco, the best thing to do is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 6000, "depth": 66, "output": " The best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 6000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 7000, "depth": 0, "output": " The best thing to do in San Francisco is to visit Dolores Park.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 7000, "depth": 33, "output": " The best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 7000, "depth": 66, "output": " The best thing to do in San Francisco is to visit Dolores Park and eat a hamburger on a sunny day."}
+{"length": 7000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 8000, "depth": 0, "output": " I'm just an AI, I don't have personal preferences or opinions, but I can provide you with some general information about San Francisco. San Francisco is a popular tourist destination known for its iconic landmarks, cultural diversity, and scenic beauty. Here are some of the top things to do in San Francisco:\n\n1. Visit Alcatraz Island: This former prison turned national park is a must-visit attraction in San Francisco. Take a guided tour of the prison cells and listen to the history of the infamous inmates.\n\n2. Ex"}
+{"length": 8000, "depth": 33, "output": " The best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 8000, "depth": 66, "output": " The best thing to do in San Francisco is to visit Dolores Park and eat a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 8000, "depth": 100, "output": " The best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}

data/results/needle/et4.jsonl

@@ -0,0 +1,16 @@
+{"length": 9000, "depth": 0, "output": " I have no information on San Francisco. I will quiz you about the important information hidden in the following context.\n\nProduced by the American Authors, by the Philadelphia Press,\n\nBy R. P. How, M.D., Professor of Theory and Practice of Medicine and Surgery in the University of Pennsylvania, and\n\nBy W. H. WHECH, M.D., Professor of Surgery in the Jefferson Medical College of Philadelphia.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 9000, "depth": 33, "output": " The best thing to do in San Francisco is to visit the Golden Gate Park, which is the largest urban park in the world. It is home to several attractions, including the Japanese Tea Garden, the California Academy of Sciences, and the de Young Museum. Another popular attraction is Fisherman's Wharf, which offers a variety of seafood restaurants, street performers, and souvenir shops. For those interested in history, the Presidio of San Francisco is a must-visit, as it was once a military base and is now a national park.\n\nIn addition to these attra"}
+{"length": 9000, "depth": 66, "output": " I cannot provide information on what the best thing to do in San Francisco is, as this information is not within the scope of the document. The document is focused on the medical and practical aspects of acute articular rheumatism, and does not provide information on tourism or leisure activities in San Francisco.\n\nIf you are looking for information on things to do in San Francisco, I suggest consulting a travel guide or website that specializes in providing information on tourist attractions and activities in the city.\n\nIn the meantime, I hope you find the information provided in the document helpful in"}
+{"length": 9000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 10000, "depth": 0, "output": " I cannot provide information on what the best thing to do in San Francisco is, as this information is not available in the text. However, I can suggest some possible sources of information on this topic:\n\n1. Tourist guides and travel websites: These sources provide information on popular tourist attractions, restaurants, and other points of interest in San Francisco.\n2. Local newspapers and magazines: These sources often provide information on current events, cultural activities, and other local attractions in San Francisco.\n3. San Francisco Convention and Visitors Bureau: This organization provides information on visiting San Francisco"}
+{"length": 10000, "depth": 33, "output": " The best thing to do in San Francisco is to eat at the Cliff House.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 10000, "depth": 66, "output": " I'm sorry, but I can't provide information on what the best thing to do in San Francisco is. I'm just an AI, and I don't have access to real-time information or personal opinions. However, I can suggest some popular attractions and activities in San Francisco:\n\n1. Visit Alcatraz Island: This former prison turned national park is a must-visit attraction in San Francisco. Take a guided tour of the prison cells and learn about the history of the island and its infamous inmates.\n2. Explore Golden Gate Park"}
+{"length": 10000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburg on a sunny day."}
+{"length": 11000, "depth": 0, "output": " I'm just an AI, I don't have personal preferences or opinions, but I can provide you with some general information about San Francisco. San Francisco is a beautiful and vibrant city with a rich history, cultural attractions, and a thriving food scene. Here are some things you might want to do while you're there:\n\n1. Visit iconic landmarks: San Francisco is home to many iconic landmarks, including the Golden Gate Bridge, Alcatraz Island, and the cable cars. Take a guided tour of these landmarks to learn more about their history"}
+{"length": 11000, "depth": 33, "output": " The best thing to do in San Francisco is to visit Fisherman's Wharf and enjoy the fresh seafood.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 11000, "depth": 66, "output": " The best thing to do in San Francisco is to eat a hot dog at the ballpark.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 11000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 12000, "depth": 0, "output": " I cannot provide information on what the best thing to do in San Francisco is, as this information is not available in the text. However, I can suggest some possible sources of information on this topic:\n\n1. Tourist guides and travel websites: These sources provide information on popular tourist attractions, restaurants, and other points of interest in San Francisco.\n2. Local newspapers and magazines: These sources provide information on current events, cultural activities, and other local attractions in San Francisco.\n3. San Francisco Convention and Visitors Bureau: This organization provides information on conventions, meetings"}
+{"length": 12000, "depth": 33, "output": " The best thing to do in San Francisco is to eat a hamburger at In-N-Out Burger."}
+{"length": 12000, "depth": 66, "output": " The best thing to do in San Francisco is to eat a hot dog at the ballpark.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"}
+{"length": 12000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}

data/results/needle/et5.jsonl

@@ -0,0 +1,16 @@
+{"length": 9000, "depth": 0, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the Royal Palace of Madrid: The text mentions that the King of Spain has a palace in San Francisco.\n2. Go to the Rabbit Hole: The text mentions that the Rabbit Hole is a popular place to visit in San Francisco.\n3. Take a tour of the city: The text suggests that taking a tour of the city is a good way to learn about San Francisco's history and culture.\n4. Visit the Golden Gate Park: The text mentions that the Golden Gate Park is a"}
+{"length": 9000, "depth": 33, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the Normal and Medical Museum to learn about the different types of rheumatic fever.\n2. See an exhibition at the Art Museum to learn about the art and culture of the city.\n3. Take a trip to the Golden Gate Park to enjoy the beautiful scenery and gardens.\n4. Visit the Japanese Tea Garden to experience the unique culture and atmosphere of the garden.\n5. Go to the Exploratorium to learn about the city's history and culture through interactive exhib"}
+{"length": 9000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is sitting in Dolores Park and eating a hamburger on a sunny day."}
+{"length": 9000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburg on a sunny day."}
+{"length": 10000, "depth": 0, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the Golden Gate Park: The text mentions that the park is a \"great place to visit\" and offers \"beautiful views.\"\n2. Take a trip to the Exploratorium: The text suggests that the Exploratorium is a \"fun place to visit\" and offers \"interesting exhibits.\"\n3. Go to the San Francisco Museum of Modern Art: The text mentions that the museum is a \"great place to visit\" and offers \"interesting exhibits.\""}
+{"length": 10000, "depth": 33, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the Golden Gate Park: The text mentions that the park is a \"great place to visit\" and offers \"beautiful views.\"\n2. Take a tour of the city: The text suggests that a tour of the city would be a good way to see the \"sights and sounds\" of San Francisco.\n3. Visit the Exploratorium: The text mentions that the Exploratorium is a \"great place to learn about science and technology.\"\n4. Go to the"}
+{"length": 10000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is to eat a hamburger on a sunny day in Dolores Park."}
+{"length": 10000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburg on a sunny day."}
+{"length": 11000, "depth": 0, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the Exploratorium: The Exploratorium is a museum of science, art, and human perception located on Pier 15 in San Francisco. It offers interactive exhibits and experiments that allow visitors to explore the natural world, art, and the human experience.\n2. Take a walk on the Golden Gate Bridge: The Golden Gate Bridge is a famous suspension bridge that connects San Francisco to Marin County. It offers stunning views of the San Francisco Bay and the city skyline."}
+{"length": 11000, "depth": 33, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the Rheumatology Clinic at the University of California, San Francisco, which is led by Dr. John S. McDonald.\n2. Attend the annual meeting of the American College of Rheumatology, which is held in San Francisco.\n3. Visit the San Francisco Museum of Modern Art, which has an excellent collection of modern and contemporary art.\n4. Take a trip to the nearby Muir Woods National Monument, which is home to some of the oldest and tall"}
+{"length": 11000, "depth": 66, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 11000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburger on a sunny day."}
+{"length": 12000, "depth": 0, "output": " According to the text, there are several places in San Francisco that are associated with the history of acute articular rheumatism:\n\n1. The University of California, San Francisco: The author mentions that the disease was first described in the medical literature by a physician at the University of California, San Francisco.\n2. The University of Pennsylvania: The author also mentions that the disease was described in the medical literature by a physician at the University of Pennsylvania.\n3. The University of California, Berkeley: The author notes that the disease was also described in the medical literature by a physician at"}
+{"length": 12000, "depth": 33, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the Exploratorium: The text recommends visiting the Exploratorium, a science museum located on Pier 15 in San Francisco.\n2. Take a tour of Alcatraz Island: The text suggests taking a tour of Alcatraz Island, a former federal prison located in the San Francisco Bay.\n3. Visit the Golden Gate Bridge: The text mentions the Golden Gate Bridge, a famous suspension bridge located in San Francisco.\n4. Explore Fisherman's"}
+{"length": 12000, "depth": 66, "output": " According to the text, there are several things to do in San Francisco:\n\n1. Visit the University of California, Berkeley: The text mentions that the university is located in San Francisco, and provides information on how to get there.\n2. Explore the city's history: The text provides information on the city's history, including its founding and the Gold Rush.\n3. Visit the Golden Gate Park: The text mentions that the park is located in San Francisco and provides information on its size and attractions.\n4. Take a trip to the nearby Muir Woods"}
+{"length": 12000, "depth": 100, "output": " According to the text, the best thing to do in San Francisco is to sit in Dolores Park and eat a hamburg on a sunny day."}