像医生一样推理：通过诊断推理过程对齐改进医疗对话系统
Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment
AI总结：
针对现有医疗对话系统模拟临床诊断时忽略内部思维过程与医生偏好对齐的问题，提出Emulation框架，通过溯因、演绎推理分析及思维过程建模生成符合医生偏好的回复，在两个数据集上验证有效且可提升问诊透明度。
摘要：
医疗对话系统因具备充当医疗助手的潜力而备受关注。让这类医疗系统模拟临床医生的诊断推理过程，一直是长期的研究重点。以往研究通过在高质量对话数据集上微调语言模型，初步实现了对临床医生诊断过程的模拟，但它们过度关注临床医生推理过程的结果，却忽略了其内部思维过程以及与临床医生偏好的对齐。本研究旨在构建一个与临床医生诊断推理过程对齐的医疗对话系统，我们提出了名为Emulation的新型框架，该框架通过思维过程建模，依托溯因和演绎诊断推理分析生成恰当回复，并与临床医生偏好对齐。在两个数据集上的实验结果证实了Emulation的有效性。关键在于，我们的框架能为生成的回复提供清晰解释，提升了医疗问诊中的透明度。
英文摘要：
Medical dialogue systems have attracted significant attention for their potential to act as medical assistants. Enabling these medical systems to emulate clinicians' diagnostic reasoning process has been the long-standing research focus. Previous studies rudimentarily realized the simulation of clinicians' diagnostic process by fine-tuning language models on high-quality dialogue datasets. Nonetheless, they overly focus on the outcomes of the clinician's reasoning process while ignoring their internal thought processes and alignment with clinician preferences. Our work aims to build a medical dialogue system that aligns with clinicians' diagnostic reasoning processes. We propose a novel framework, Emulation, designed to generate an appropriate response that relies on abductive and deductive diagnostic reasoning analyses and aligns with clinician preferences through thought process modeling. Experimental results on two datasets confirm the efficacy of Emulation. Crucially, our framework furnishes clear explanations for the generated responses, enhancing its transparency in medical consultations.
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。
