@inproceedings{chen-etal-2025-medfact,
    title = "{M}ed{F}act: A Large-scale {C}hinese Dataset for Evidence-based Medical Fact-checking of {LLM} Responses",
    author = "Chen, Tong  and
      Wang, Zimu  and
      Miao, Yiyi  and
      Luo, Haoran  and
      Yuanfei, Sun  and
      Wang, Wei  and
      Jiang, Zhengyong  and
      Sen, Procheta  and
      Su, Jionglong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1646/",
    pages = "32328--32341",
    ISBN = "979-8-89176-332-6",
    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."
}