UTAustin-AIHealth/MedHallu
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:… See the full description on the dataset page: https://huggingface.co/datasets/UTAustin-AIHealth/MedHallu.
| Author / organization | UTAustin-AIHealth |
|---|---|
| Downloads | 4.3K (as reported by Hugging Face) |
| Likes | 25 |
| Created | 19 February 2025 |
| Last updated at source | 21 February 2025 |
Recent changes
- 16 Oct 2026 Description: 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 → 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
Changes detected by Silytics between refreshes of the source.
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 16 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.