Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introduce TCM3CEval, a benchmark assessing LLMs in TCM across three dimensions: core knowledge mastery, classical text understanding, and clinical decision-making. We evaluate diverse models, including international (e.g., GPT-4o), Chinese (e.g., InternLM), and medical-specific (e.g., PLUSE). Results show a performance hierarchy: all models have limitations in specialized subdomains like Meridian & Acupoint theory and Various TCM Schools, revealing gaps between current capabilities and clinical needs. Models with Chinese linguistic and cultural priors perform better in classical text interpretation and clinical reasoning. TCM-3CEval sets a standard for AI evaluation in TCM, offering insights for optimizing LLMs in culturally grounded medical domains. The benchmark is available on Medbench's TCM track, aiming to assess LLMs' TCM capabilities in basic knowledge, classic texts, and clinical decision-making through multidimensional questions and real cases.

本研究针对大型语言模型在传统中医学评估不足的问题，提出了TCM-3CEval基准，评估模型在核心知识、经典文本理解和临床决策三个维度的表现。研究发现，具备中文语言和文化背景的模型在经典文本解读和临床推理方面表现更佳，TCM-3CEval为中医领域的AI评估设定了标准，为优化大型语言模型提供了有价值的见解。

中医三维评估：评估大型语言模型在传统中医学中的回应的三轴基准