README.md

# UTAustin-AIHealth

Welcome to **UTAustin-AIHealth** – a hub dedicated to advancing research in medical AI. 
This repo contains the **MedHallu** dataset, which underpins our recent work:

**MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models**

MedHallu is a rigorously designed benchmark intended to evaluate large language models' ability to detect hallucinations in medical question-answering tasks. 
The dataset is organized into two distinct splits:

- **pqa_labeled:** Contains 1,000 high-quality, human-annotated samples derived from PubMedQA.
- **pqa_artificial:** Contains 9,000 samples generated via an automated pipeline from PubMedQA.

---

## Setup Environment

To work with the MedHallu dataset, please install the Hugging Face `datasets` library using pip:

```bash
pip install datasets
```

## How to Use MedHallu

**Downloading the Dataset:**  
```python
from datasets import load_dataset

# Load the 'pqa_labeled' split: 1,000 high-quality, human-annotated samples.
medhallu_labeled = load_dataset("UTAustin-AIHealth/MedHallu", "pqa_labeled")

# Load the 'pqa_artificial' split: 9,000 samples generated via an automated pipeline.
medhallu_artificial = load_dataset("UTAustin-AIHealth/MedHallu", "pqa_artificial")
```

---


## License

This dataset and associated resources are distributed under the [MIT License](https://opensource.org/license/mit/).

## Citations

If you find MedHallu useful in your research, please consider citing our work:

```bibtex
@misc{pandit2025medhallucomprehensivebenchmarkdetecting,
      title={MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models}, 
      author={Shrey Pandit and Jiawei Xu and Junyuan Hong and Zhangyang Wang and Tianlong Chen and Kaidi Xu and Ying Ding},
      year={2025},
      eprint={2502.14302},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.14302}, 
}
```

## Contact
For further information or inquiries about MedHallu, please reach out at shreypandit@utexas.edu