MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts
arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory compliance. We introduce MedFact, a challenging Chinese medical fact-checking benchmark with 2,116 expert-annotated instances from diverse real-world texts, spanning 13 specialties, 8 error types, 4 writing styles, and 5 difficulty levels. Construction uses a hybrid AI-human framework where iterative expert feedback refines AI-driven, multi-criteria filtering to ensure high quality and difficulty. We
The deployment of LLMs in critical sectors like medicine necessitates robust fact-checking benchmarks, particularly in large and distinct language domains such as Chinese, to ensure safety and compliance.
This benchmark highlights the crucial need for verifiable AI behavior in high-stakes applications, setting a standard for future medical AI deployments and potentially influencing regulatory frameworks.
The availability of a specialized, expert-annotated benchmark for Chinese medical texts elevates the scrutiny and development standards for LLMs in this domain, pushing for higher accuracy and safety.
- · AI Safety Researchers
- · Medical AI Developers (China)
- · Healthcare Regulators
- · Patients
- · Undeveloped LLMs in Medicine
- · Providers of Unverified AI Solutions
Improved reliability and trustworthiness of Large Language Models specifically in Chinese medical applications due to standardized testing.
Increased investment and research into fact-checking mechanisms and explainable AI for medical applications globally, recognizing the inherent risks.
The establishment of similar expert-annotated, language-specific medical benchmarks becoming standard practice across other non-English language markets, fragmenting AI development but increasing safety.
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Read at arXiv cs.AI