模型对抗与协作:一个辩论情报框架,用于在大型语言模型中增强医学推理
Xinti Sun1, Qiyang Hong1, Mengyan Zhang1
1State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cell reports. Medicine
|January 6, 2026
概括
模型对抗与协作 (MCC) 通过对各种大型语言模型 (LLM) 进行辩论和完善答案来增强医学推理. 这种AI框架显著提高了诊断准确性和医疗问题答案性能.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 医疗保健的自然语言处理.
背景情况:
- 医学推理对于临床决策至关重要,包括诊断和治疗规划.
- 在心理学中,同行互动增强了自我纠正,这表明AI模型改进的潜力.
- 现有的人工智能组合方法缺乏代改进和自我反思能力.
研究的目的:
- 引入模型对抗与协作 (MCC),这是人工智能的新型辩论情报框架.
- 为了提高医疗推理,利用各种大型语言模型 (LLM) 之间的结构化多轮辩论.
- 提高医疗问答和诊断任务的准确性和性能.
主要方法:
- 开发了MCC,一个整合批评和自我反思的框架,用于代的LLM推理改进.
- 实施结构化,多层面的对抗和不同LLM之间的合作.
- 根据多选项基准 (MedQA,PubMedQA,MMLU医疗子集) 和长形式的医疗QA评估MCC.
主要成果:
- 在MedQA (92.6%) 和PubMedQA (84.8%) 上,MCC实现了高准确度,超过了个别的LLMs.
- 在长形式的质量保证中,MCC在医生和非专业人士的评估中超过了所有个人LLM和Med-PaLM 2.
- 在诊断对话中,MCC表现出卓越的表现,达到80%的top-1诊断率.
结论:
- MCC是一个可扩展的,模型不可知框架,可以显著提高医学推理能力.
- 通过MCC,LLM之间的协作审议可以提高医疗问题的答案和诊断的准确性.
- 该框架显示了改善人工智能驱动的临床决策支持的前景.
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