MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models
This provides a standardized benchmark for evaluating medical LLMs in the Chinese domain, aiding the medical research community, though it is incremental as it adapts existing benchmark concepts to a specific language and domain.
The authors tackled the lack of unified evaluation standards for medical large language models (LLMs) by introducing MedBench, a large-scale Chinese benchmark with 40,041 questions from medical exams and real-world cases, and found that Chinese medical LLMs underperform while some general-domain LLMs show considerable medical knowledge.
The emergence of various medical large language models (LLMs) in the medical domain has highlighted the need for unified evaluation standards, as manual evaluation of LLMs proves to be time-consuming and labor-intensive. To address this issue, we introduce MedBench, a comprehensive benchmark for the Chinese medical domain, comprising 40,041 questions sourced from authentic examination exercises and medical reports of diverse branches of medicine. In particular, this benchmark is composed of four key components: the Chinese Medical Licensing Examination, the Resident Standardization Training Examination, the Doctor In-Charge Qualification Examination, and real-world clinic cases encompassing examinations, diagnoses, and treatments. MedBench replicates the educational progression and clinical practice experiences of doctors in Mainland China, thereby establishing itself as a credible benchmark for assessing the mastery of knowledge and reasoning abilities in medical language learning models. We perform extensive experiments and conduct an in-depth analysis from diverse perspectives, which culminate in the following findings: (1) Chinese medical LLMs underperform on this benchmark, highlighting the need for significant advances in clinical knowledge and diagnostic precision. (2) Several general-domain LLMs surprisingly possess considerable medical knowledge. These findings elucidate both the capabilities and limitations of LLMs within the context of MedBench, with the ultimate goal of aiding the medical research community.