医学和工程教育中的开源和封闭源LLM
Liping Sun1, Ya Li1, Hongxing Kan1
1School of Medical Informatics Engineering, Anhui University of Chinese Medicine, Hefei, Anhui, China.
Frontiers in medicine
|January 29, 2026
概括
开源大型语言模型 (LLM) 在教育中显示出前景. 快速工程显著提高了医学和工程学生的LLM准确性,而DeepSeek则非常出色.
科学领域:
- 教育中的人工智能
- 开源大型语言模型 (LLM)
- 在STEM教育方面.
背景情况:
- 像GPT-4这样的大型语言模型 (LLM) 正在改变教育.
- 开源的LLM提供了可访问性和定制性,但在医学和工程领域未得到充分使用.
- 需要进行系统的评估来比较这些领域的开源和封闭源LLM.
研究的目的:
- 评估开源LLM (DeepSeek,GLM-4,Kimi) 与医学和工程教育的GPT-4的性能.
- 调查快速工程策略对LLM准确性的影响.
- 突出在学术环境中开源LLMs的潜力.
主要方法:
- 对DeepSeek,GLM-4,Kimi和GPT-4在不同类型的问题上的比较性能分析.
- 快速工程技术的实施和测试 (角色扮演,生成知识的提示,思维链,几次拍摄,输出风格).
- 培训后对LLM答案的准确性评估.
主要成果:
- 与其他模型相比,DeepSeek在所有问题类型中表现出卓越的表现.
- 快速工程显著提高了LLM的准确性,而DeepSeek的准确性超过95%.
- 在所有四个LLM中,短答题的准确度最高 (高达97%),这凸显了快速工程的作用.
结论:
- 开源的LLM,特别是DeepSeek,是医学和工程教育的有效工具.
- 快速工程对于最大限度地提高LLM在学术问题解决中的实用性至关重要.
- 建议对开源LLM进行更广泛的整合,以弥合目前的评估差距,并加强教育支持.
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