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arXiv · 2509.17436

MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses

Tong Chen·Zimu Wang·Yiyi Miao·Haoran Luo·Yuanfei Sun·Wei Wang·Zhengyong Jiang·Procheta Sen·Jionglong Su
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Abstract

Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated content, leaving the verification of content generated by large language models (LLMs) relatively unexplored. To address this gap, we introduce MedFact, the first evidence-based Chinese medical fact-checking dataset of LLM-generated medical content. It consists of 1,321 questions and 7,409 claims, mirroring the complexities of real-world medical scenarios. We conduct comprehensive experiments in both in-context learning (ICL) and fine-tuning settings, showcasing the capability and challenges of current LLMs on this task, accompanied by an in-depth error analysis to point out key directions for future research. Our dataset is publicly available at https://github.com/AshleyChenNLP/MedFact.

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Tong Chen, Zimu Wang, Yiyi Miao, Haoran Luo, Yuanfei Sun, Wei Wang, Zhengyong Jiang, Procheta Sen, Jionglong Su. 2025-09-22. MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses. https://arxiv.org/abs/2509.17436

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