在西班牙语医学能力考试中,单代理和多代理语言模型的性能
Fernando R Altermatt1, Andres Neyem2,3, Nicolas Sumonte2,3
1Division of Anesthesiology, School of Medicine, Pontificia Universidad Católica de Chile, Marcoleta 377, 8320000, Santiago, RM, Chile. falterma@uc.cl.
BMC medical education
|May 7, 2025
概括
多代理策略,特别是MDAGENTS,显著提高了西班牙医疗考试中的GPT-4o表现. 更简单的方法也回答了许多问题,显示了LLM.
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.
- 医疗教育 技术 技术 医学教育
背景情况:
- 大型语言模型 (LLM) 在医疗决策和教育方面显示出潜力.
- 在西班牙医疗环境中LLM的表现在很大程度上是未被探索的.
- 本研究评估了在智利医疗执照考试 (EUNACOM) 上的LLM策略.
研究的目的:
- 评估单代理和多代理的LLM策略,以回答西班牙医学考试问题.
- 为了比较不同提示和代理协作技术的有效性.
- 分析21个医学专业的LLM绩效.
主要方法:
- 在1062个EUNACOM多选题中进行了GPT-4o测试.
- 评估的策略:零射击,少数射击,思维链 (CoT),自我反思,MED-PROMPT,投票,加权投票,博达计数,媒介,媒介.
- 通过统计分析,在三个温度设置 (0.3,0.6,1.2) 中评估了准确性.
主要成果:
- MDAGENTS实现了最高的准确性 (89.97%),超过了所有其他策略 (p < 0.001).
- 梅达根斯 (87.99%) 和CoT与Few-Shot (87.67%) 也表现出很高的表现.
- 温度设置没有显著影响结果;在精神病学,神经学和手术中准确度最高.
结论:
- 多代理策略,特别是MDAGENTS,显著提高了在西班牙医学考试上的LLM绩效.
- 对于许多问题,更简单的单个代理策略就足够了,这表明对复杂推理的需求有限.
- 士课程为西班牙语医疗保健提供可扩展的工具,以计算优化为未来研究重点.
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