Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies

• URL: http://arxiv.org/abs/2503.07306v1
• Date: Mon, 10 Mar 2025 13:28:25 GMT
• Title: Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies
• Authors: Luyi Jiang, Jiayuan Chen, Lu Lu, Xinwei Peng, Lihao Liu, Junjun He, Jie Xu,
• Abstract summary: This study introduces a granular error taxonomy through systematic analysis of top 10 models on MedBench.<n> Evaluation of 10 leading models reveals vulnerabilities, despite achieving 0.86 accuracy in medical knowledge recall.<n>Our analysis uncovers systemic weaknesses in knowledge boundary enforcement and multi-step reasoning.
• Score: 11.0505830548286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evaluation and improvement of medical large language models (LLMs) are critical for their real-world deployment, particularly in ensuring accuracy, safety, and ethical alignment. Existing frameworks inadequately dissect domain-specific error patterns or address cross-modal challenges. This study introduces a granular error taxonomy through systematic analysis of top 10 models on MedBench, categorizing incorrect responses into eight types: Omissions, Hallucination, Format Mismatch, Causal Reasoning Deficiency, Contextual Inconsistency, Unanswered, Output Error, and Deficiency in Medical Language Generation. Evaluation of 10 leading models reveals vulnerabilities: despite achieving 0.86 accuracy in medical knowledge recall, critical reasoning tasks show 96.3% omission, while safety ethics evaluations expose alarming inconsistency (robustness score: 0.79) under option shuffled. Our analysis uncovers systemic weaknesses in knowledge boundary enforcement and multi-step reasoning. To address these, we propose a tiered optimization strategy spanning four levels, from prompt engineering and knowledge-augmented retrieval to hybrid neuro-symbolic architectures and causal reasoning frameworks. This work establishes an actionable roadmap for developing clinically robust LLMs while redefining evaluation paradigms through error-driven insights, ultimately advancing the safety and trustworthiness of AI in high-stakes medical environments.

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