MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts

• URL: http://arxiv.org/abs/2509.12440v1
• Date: Mon, 15 Sep 2025 20:46:21 GMT
• Title: MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts
• Authors: Jiayi He, Yangmin Huang, Qianyun Du, Xiangying Zhou, Zhiyang He, Jiaxue Hu, Xiaodong Tao, Lixian Lai,
• Abstract summary: We introduce MedFact, a new benchmark for Chinese medical fact-checking.<n>It comprises 2,116 expert-annotated instances curated from diverse real-world texts.<n>It employs a hybrid AI-human framework where expert feedback refines an AI-driven, multi-criteria filtering process.
• Score: 4.809421212365958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing deployment of Large Language Models (LLMs) in healthcare necessitates a rigorous evaluation of their factual reliability. However, existing benchmarks are often limited by narrow domains of data, failing to capture the complexity of real-world medical information. To address this critical gap, we introduce MedFact, a new and challenging benchmark for Chinese medical fact-checking. MedFact comprises 2,116 expert-annotated instances curated from diverse real-world texts, spanning 13 medical specialties, 8 fine-grained error types, 4 writing styles, and multiple difficulty levels. Its construction employs a hybrid AI-human framework where iterative expert feedback refines an AI-driven, multi-criteria filtering process, ensuring both high data quality and difficulty. We conduct a comprehensive evaluation of 20 leading LLMs, benchmarking their performance on veracity classification and error localization against a human expert baseline. Our results reveal that while models can often determine if a text contains an error, precisely localizing it remains a substantial challenge, with even top-performing models falling short of human performance. Furthermore, our analysis uncovers a frequent ``over-criticism'' phenomenon, a tendency for models to misidentify correct information as erroneous, which is exacerbated by advanced reasoning techniques such as multi-agent collaboration and inference-time scaling. By highlighting these critical challenges for deploying LLMs in medical applications, MedFact provides a robust resource to drive the development of more factually reliable and medically aware models.

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