对基于思维链的快速工程技术进行比较评估,用于医学问题解答
1Interdisciplinary Program of Medical Informatics, College of Medicine, Seoul National University, Republic of Korea.
Computers in biology and medicine
|July 2, 2025
概括
思想链 (Chain-of-Thought,CoT) 提示并没有显著改善医学推理中的大语言模型 (LLM) 性能. 简单的CoT方法可能对临床AI应用更有效,数据集和模型架构是关键因素.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 显示出增强临床决策的前景.
- 评估先进提示技术 (如思维链 (CoT)) 对医学推理的影响对于医学中的AI至关重要.
- 了解不同Cot方法如何影响临床环境中的LLM绩效至关重要.
研究的目的:
- 评估各种思维链 (CoT) 提示技术对五种大型语言模型 (LLM) 医学推理能力的影响.
- 确定不同Cot提示策略在医疗问答任务中的临床适用性.
- 在不同的临床和非临床数据集中使用CoT提示来比较LLM的性能.
主要方法:
- 五个LLM (GPT-4o-mini,GPT-3.5-turbo,o1-mini,Gemini-1.5-Flash,Gemini-1.0-pro) 使用API访问进行了评估.
- 采用了具有明显认知特征的思维链 (CoT) 提示方法.
- 在临床数据集 (例如,EHRNoteQA,MedMCQA) 和非临床数据集上使用代质量保证系统来评估性能.
主要成果:
- 在使用MIMIC-IV排放摘要的EHRNoteQA上,o1-mini模型获得了最高的准确度 (88.4%).
- 传统的Cot提示在临床数据集中产生了稳定的结果.
- 交互式CoT显示出可变的性能,在复杂的任务中表现不佳 (MedMCQA:61.7%),但在临床笔记上表现良好 (EHRNoteQA:83.5%).
- 统计分析表明,提示方法之间没有显著差异 (p > .05),尽管效果大小有所不同.
结论:
- 复杂的提示技术与医学推理中LLM的简单方法相比,没有显著的性能提升.
- 数据集特征和LLM架构在性能方面发挥的作用比复杂的提示更为关键.
- 更简单的CoT方法被认为可能更有效用于实际的临床AI应用.
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