RuleAlign：通过诊断规则对齐使大型语言模型成为更好的医生
RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment
AI总结：
本文提出RuleAlign框架，通过构建医疗对话数据集和偏好学习方法，使大型语言模型与诊断规则对齐，提升其像医生一样高效收集信息和推理诊断的能力。
摘要：
GPT-4、MedPaLM-2和Med-Gemini等大型语言模型（LLMs）在各类医疗基准上的表现已与人类专家相媲美。然而，它们在像医生一样进行专业诊断时仍面临挑战，特别是在高效收集患者信息和推理最终诊断方面。为此，我们提出了RuleAlign框架，旨在使LLMs与特定诊断规则对齐。我们开发了一个医疗对话数据集，包含患者与医生之间基于规则的交流，并设计了一种通过偏好学习实现的对齐学习方法。实验结果证明了所提方法的有效性。我们希望这项工作能启发人们探索LLMs作为AI医生的潜力。
英文摘要：
Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve performance competitively with human experts across various medical benchmarks. However, they still face challenges in making professional diagnoses akin to physicians, particularly in efficiently gathering patient information and reasoning the final diagnosis. To this end, we introduce the RuleAlign framework, designed to align LLMs with specific diagnostic rules. We develop a medical dialogue dataset comprising rule-based communications between patients and physicians and design an alignment learning approach through preference learning. Experimental results demonstrate the effectiveness of the proposed approach. We hope that our work can serve as an inspiration for exploring the potential of LLMs as AI physicians.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
