大型语言模型在临床记录纠正中的应用:对各种再培训方法的综合研究
Ana M Maitin1, Alberto Nogales1, Sergio Fernández-Rincón1
1CEIEC, Universidad Francisco de Vitoria, Pozuelo de Alarcón, 28223 Madrid, Spain.
Journal of the American Medical Informatics Association : JAMIA
|December 21, 2024
概括
大型语言模型 (LLM) 在评估医疗教育临床记录方面表现有前途. 微调和低级调整等技术使开源模型与专有模型相匹配.
科学领域:
- 人工智能在医学中的应用
- 在医疗保健中的自然语言处理.
- 医疗教育 技术 技术 医学教育
背景情况:
- 临床记录 (CR) 评估对于医疗培训和诊断至关重要.
- 评估大型语言模型 (LLM) 在自主评估CR的有效性是一个新兴领域.
- 像GPT-3.5和GPT-4这样的专有模型提供了高级功能,但开源替代方案需要优化.
研究的目的:
- 评估各种LLM (GPT-3.5,GPT-4,Llama-2 7B,13B) 在自主评估临床记录中的有效性.
- 在开源模型上比较不同的适应技术 (快速工程,微调,LoRA).
- 通过CR分析确定LLMs在加强医学教育和诊断技能方面的潜力.
主要方法:
- 评估了GPT-3.5,GPT-4,拉玛-2 13B和拉玛-2 7B模型.
- 在Llama-2 7B.上实现并比较了快速工程,微调 (FT) 和低级调整 (LoRA).
- 使用英语和西班牙语提示程序评估模型性能,与专家评估进行基准测试.
主要成果:
- GPT-4的表现与专家评估相提并论.
- 在Llama-2 7B上微调 (FT) 改进了西班牙语文本的理解到Llama-2 13B的英语提示级别.
- 低级适应 (LoRA),特别是FT,显著提高了性能,超过了GPT-3.5.5.的表现.
结论:
- GPT-4表现出强的性能,但FT和LoRA对于优化Llama-2 7B等开源LLM至关重要.
- 低级调整是一种高度有效的技术,可以增强开源模型的能力.
- 法律学士学位提供了一个有前途的,创新的临床记录纠正和医学教育方法.
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