README.md

---
language:
- bn
license: unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- question-answering
dataset_info:
- config_name: additional
  features:
  - name: id
    dtype: string
  - name: question_stem
    dtype: string
  - name: choices
    sequence:
    - name: text
      dtype: string
    - name: label
      dtype: string
---

## Data Summary
This is the Bangla-translated version of the [OpenBookQA](https://huggingface.co/datasets/allenai/openbookqa) dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications. This method enhances the semantic accuracy and expressiveness of the translated content. OpenBookQA focuses on advanced question-answering, requiring multi-step reasoning, additional common and commonsense knowledge, and rich text comprehension, similar to open-book exams.

## Data Details
### Data Instances
### Defaults
An example of a 'train' looks as follows:

```json
{
  "question_stem": "রাতে যখন একটি গাড়ি আপনার কাছে আসছে",
  "choices": {
    "text": ["হেডলাইট আরো তীব্র হয়", "হেডলাইট অন্ধকারে ফিরে যায়", "হেডলাইট একটি ধ্রুবক থাকে", "হেডলাইট বন্ধ"],
    "label": ["A", "B", "C", "D"]
  },
  "answerKey": "A"
}
```

### Data Fields
#### default
The data fields are the same among all splits.

- `id`: a `string` feature.
- `question_stem`: a `string` feature.
- `choices`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `label`: a `string` feature.
- `answerKey`: a `string` feature.

## Data Split
| Split | Number |
| ---- | ----- |
| train | 4947 |
| validation | 500 |
| test | 497 |

## Citation
[TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking](https://huggingface.co/papers/2502.11187)

Github Repository: [https://github.com/hishab-nlp/lm-evaluation-harness](https://github.com/hishab-nlp/lm-evaluation-harness)

```
@misc{nahin2025titullmsfamilybanglallms,
      title={TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking}, 
      author={Shahriar Kabir Nahin and Rabindra Nath Nandi and Sagor Sarker and Quazi Sarwar Muhtaseem and Md Kowsher and Apu Chandraw Shill and Md Ibrahim and Mehadi Hasan Menon and Tareq Al Muntasir and Firoj Alam},
      year={2025},
      eprint={2502.11187},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.11187}, 
}
```