Duplicate from UTAustin-AIHealth/MedHallu

Co-authored-by: Shrey Pandit <SP2001@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+- config_name: pqa_artificial
+  features:
+  - name: Question
+    dtype: string
+  - name: Knowledge
+    sequence: string
+  - name: Ground Truth
+    dtype: string
+  - name: Difficulty Level
+    dtype: string
+  - name: Hallucinated Answer
+    dtype: string
+  - name: Category of Hallucination
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 18206610
+    num_examples: 9000
+  download_size: 10283429
+  dataset_size: 18206610
+- config_name: pqa_labeled
+  features:
+  - name: Question
+    dtype: string
+  - name: Knowledge
+    sequence: string
+  - name: Ground Truth
+    dtype: string
+  - name: Difficulty Level
+    dtype: string
+  - name: Hallucinated Answer
+    dtype: string
+  - name: Category of Hallucination
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1979216
+    num_examples: 1000
+  download_size: 1116475
+  dataset_size: 1979216
+configs:
+- config_name: pqa_artificial
+  data_files:
+  - split: train
+    path: pqa_artificial/train-*
+- config_name: pqa_labeled
+  data_files:
+  - split: train
+    path: pqa_labeled/train-*
+---
+
+# Dataset Card for MedHallu
+
+MedHallu is a comprehensive benchmark dataset designed to evaluate the ability of large language models to detect hallucinations in medical question-answering tasks. 
+
+## Dataset Details
+
+### Dataset Description
+
+MedHallu is intended to assess the reliability of large language models in a critical domain—medical question-answering—by measuring their capacity to detect hallucinated outputs. The dataset includes two distinct splits:
+
+- **pqa_labeled:** 1,000 high-quality samples derived from PubMedQA pqa_labeled split.
+- **pqa_artificial:** 9,000 samples generated from PubMedQA pqa_artificial split.
+
+- **Curated by:** UTAustin-AIHealth  
+- **Language(s) (NLP):** English  
+- **License:** [MIT License](https://opensource.org/license/mit/)
+
+
+### Dataset Sources
+
+- **Paper:** *MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models*  
+- **Original Dataset:** Derived from PubMedQA ["qiaojin/PubMedQA"](https://huggingface.co/datasets/qiaojin/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")
+```
+
+## Dataset Structure
+
+MedHallu is organized into two main splits:
+
+- **pqa_labeled:** 1,000 high-quality samples derived from PubMedQA pqa_labeled split.
+- **pqa_artificial:** Contains 9,000 samples generated from PubMedQA pqa_artificial split.
+
+Each sample includes a medical question along with its corresponding ground truth answer, hallucinated answer, difficulty level, and the category of hallucination.
+
+## Source Data
+
+The MedHallu dataset is built upon data sourced from the PubMedQA dataset.
+
+## Citation
+
+If you use the MedHallu dataset in your research, please consider citing our work:
+
+```
+@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}, 
+}
+```

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