使用大型语言模型进行推理,用于回答医学问题
Mary M Lucas1, Justin Yang2, Jon K Pomeroy1,3
1College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, United States.
概括
集成推理是大型语言模型 (LLM) 的新提示方法,增强了医疗问题的答案准确性和一致性. 这种方法有望提高LLM的性能,特别是在能力较低的模型中.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在医疗问题解答方面显示出潜力.
- 当前的LLM推理方法可能不一致,缺乏精细化.
- 提高LLM推理对于可靠的医疗应用至关重要.
研究的目的:
- 为了研究LLM推理方法.
- 提出和评估一种称为集体推理的新提示技术.
- 通过精细的推理和减少不一致性来提高医疗问题答案的性能.
主要方法:
- 使用了USMLE样本考试中的多选题.
- 对闭源 (GPT-3.5轮机,GPT-4轮机) 和开源 (Med42-70B) 临床LLMs进行评估组合推理.
- 组合推理与零射击思维链相比较,具有自我一致性.
主要成果:
- 集合推理在多个考试步骤中在GPT-3.5轮机和Med42-70B上表现优于零射击思维链.
- GPT-4轮机显示混合结果,集团推理在第一步问题上表现出色.
- 这种方法始终提高了响应的准确性,并证明了更可靠的推理.
结论:
- 代集团推理可以显著提高医学问答中的LLM性能,特别是在较不强大的模型中.
- 该方法完善了LLM推理,提高了响应的一致性,即使在像GPT-4轮机这样的高级模型中也是如此.
- 人类-人工智能合作被确定为未来的方向,以进一步提高LLM推理能力.
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