用大型语言模型支持研究生考试准备:对传统中医教育的影响
Baifeng Wang1, Meiwei Zhang2, Zhe Wang3
1Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Frontiers in medicine
|January 26, 2026
概括
两个大型语言模型 (LLM) 超过了2023年中国传统中医药 (TCM) 研究生考试的通过分数. 这些人工智能模型显示了通过强有力的推理和知识整合来推进TCM教育的前景.
科学领域:
- 人工智能在医学中的应用
- 医疗教育 技术 技术 医学教育
- 传统中国医学 (TCM) 是一种
背景情况:
- 中国的医学教育体系将研究生考试作为职业发展的优先事项.
- 大型语言模型 (LLM) 越来越多地应用于中国传统医学 (TCM).
- 在专业的TCM考试中,LLM的成绩在很大程度上是未被探索的.
研究的目的:
- 评估和比较四个LLM (Ernie Bot,ChatGLM,SparkDesk,GPT-4) 在2023年中国TCM研究生考试 (CPE-TCM) 的表现.
- 探索LLMs在支持TCM教育和学术发展方面的潜力.
主要方法:
- 通过2023年的CPE-TCM考试评估了四个LLM.
- 计算了具体学科的分数,并分析了逻辑推理和信息使用 (内部/外部) 的反应.
主要成果:
- 埃尼·博特 (50.30%) 和聊天GLM (46.67%) 超过了通过得分.
- 聊天GLM和GPT-4展示了卓越的逻辑推理和内部信息使用.
- SparkDesk的响应正确性与内部/外部信息使用有显著的关联 (P < 0.001).
结论:
- 埃尼·博特和ChatGLM展示了重要的TCM专业知识,超过了研究生选择门.
- 士课程显示出通过逻辑推理和知识整合来增强TCM教育的巨大潜力.
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