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# UTAustin-AIHealth
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Welcome to **UTAustin-AIHealth** – a hub dedicated to advancing research in medical AI. Our flagship contribution is the **MedHallu** dataset, which underpins our recent work:
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**MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models**
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MedHallu is a rigorously designed dataset that provides a benchmark for evaluating large language models in detecting hallucinations in medical question-answering tasks. Our goal is to help researchers and practitioners improve the reliability of AI in the medical domain, thereby enhancing patient safety and clinical decision-making.
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---
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## License
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This dataset and associated resources are distributed under the **MIT License**.
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---
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## How to Use MedHallu
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- **Downloading the Dataset:**
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Detailed instructions for downloading MedHallu are provided on our website and accompanying documentation.
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- **Usage Guidelines:**
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We offer example code and tutorials to help you integrate the dataset into your evaluation pipelines. Please refer to our documentation for step-by-step guidance.
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---
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## Citations
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If you find MedHallu useful in your research, please consider citing our work:
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```bibtex
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@inproceedings{your-citation-key,
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title={MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models},
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author={Your Name and Collaborators},
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booktitle={Conference/Journal Name},
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year={2025},
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publisher={Publisher Name}
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}
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