Dataset Card for CRAFT

This is CRAFT, a dataset for Chinese Real-time statistics And Finance knowledge ediTing). CRAFT supports real-time data curation with a fully automated pipeline.

This repository contains the CRAFT dataset curated in 25Q1.

Dataset Details

Dataset Description

• Curated by: The CRAFT&KEDAS team.
• Language(s) (NLP): Chinese
• License: Apache-2.0

Dataset Sources

• Monthly statistical reports from the National Bureau of Statistics of China via the cn-stats API.
• Annual financial statements of publicly listed Chinese companies via the AKShare API.
• Commonsense data from C3.

Uses

Real-time knowledge editing. Evaluates Edit Success, Locality, and Portability.

Dataset Structure

{
  "case_id": "an integer ID",
  "subject": [
    "related subject 1",
    "related subject 2"
  ],
  "prompt": [
    "prompt 1",
    "prompt 2"
  ],
  "target_new": [
    "new target 1",
    "new target 2"
  ],
  "portability": {
    "Subject_Aliasing": [
      {
        "prompt": "subject aliasing query 1",
        "ground_truth": [
          "subject aliasing answer 1"
        ]
      },
      {
        "prompt": "subject aliasing query 2",
        "ground_truth": [
          "subject aliasing answer 2"
        ]
      }
    ],
    "Reasoning": [
      {
        "prompt": "reasoning query",
        "ground_truth": [
          "reasoning answer"
        ]
      }
    ]
  },
  "locality": {
    "Relation_Specificity": [
      {
        "prompt": "relation specificity query 1",
        "ground_truth": [
          "relation specificity answer 1"
        ]
      },
      {
        "prompt": "relation specificity query 2",
        "ground_truth": [
          "relation specificity answer 2"
        ]
      }
    ],
    "common_sense": [
      {
        "prompt": "common sense query 1",
        "ground_truth": [
          "common sense answer 1"
        ]
      },
      {
        "prompt": "common sense query 2",
        "ground_truth": [
          "common sense answer 2"
        ]
      }
    ]
  }
}

Citation

If you find our work useful, feel free to cite our paper:

@misc{tang2025aligninglanguagemodelsrealtime,
      title={Aligning Language Models with Real-time Knowledge Editing}, 
      author={Chenming Tang and Yutong Yang and Kexue Wang and Yunfang Wu},
      year={2025},
      eprint={2508.01302},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.01302}, 
}