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.
@article{arxiv.2509.17436,
title = {MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses},
author = {Tong Chen and Zimu Wang and Yiyi Miao and Haoran Luo and Yuanfei Sun and Wei Wang and Zhengyong Jiang and Procheta Sen and Jionglong Su},
journal= {arXiv preprint arXiv:2509.17436},
year = {2025}
}