TY - GEN
T1 - MEDFACT
T2 - 30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025
AU - Chen, Tong
AU - Wang, Zimu
AU - Miao, Yiyi
AU - Luo, Haoran
AU - Sun, Yuanfei
AU - Wang, Wei
AU - Jiang, Zhengyong
AU - Sen, Procheta
AU - Su, Jionglong
N1 - Publisher Copyright:
© 2025 Association for Computational Linguistics.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105040210167
U2 - 10.18653/v1/2025.emnlp-main.1646
DO - 10.18653/v1/2025.emnlp-main.1646
M3 - Conference Proceeding
AN - SCOPUS:105040210167
T3 - EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
SP - 32340
EP - 32353
BT - EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
A2 - Christodoulopoulos, Christos
A2 - Chakraborty, Tanmoy
A2 - Rose, Carolyn
A2 - Peng, Violet
PB - Association for Computational Linguistics (ACL)
Y2 - 4 November 2025 through 9 November 2025
ER -