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arXiv 2401.05695cs.CL

将医生诊断逻辑整合进大型语言模型：基于过程反馈的偏好学习

Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback

School of Computer Science, Peking University（北京大学计算机学院）；Key Laboratory of High Confidence Software Technologies(PKU), MOE, China（教育部高可信软件技术重点实验室）；Beijing Jiaotong University（北京交通大学）

Chengfeng Dou, Zhi Jin, Wenpin Jiao, Haiyan Zhao, Yongqiang Zhao, Zhenwei Tao

备注：
Accepted by ACL2024 Findings
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AI总结：

本文提出 PLPF 方法，通过规则建模、偏好数据生成与偏好对齐将医生诊断逻辑融入 LLM，显著提升多轮及单轮医疗对话的诊断准确率。

摘要：

大型语言模型在医疗对话生成中的应用已受到广泛关注，相关研究重点在于提升回复质量与流畅度。尽管以往研究在优化单轮医疗问答任务的模型性能方面取得了进展，但仍需增强模型处理多轮对话的能力，以避免逻辑不一致。为解决这一问题，我们提出一种称为基于过程反馈的偏好学习（PLPF）的方法，将医生的诊断逻辑整合到 LLM 中。PLPF 包括规则建模、偏好数据生成和偏好对齐，用于训练模型遵循诊断流程。使用标准化病人测试的实验结果表明，PLPF 将基线模型在医疗对话中的诊断准确率提升了 17.6%，优于传统的基于人类反馈的强化学习。此外，PLPF 在多轮和单轮对话任务中均表现出有效性，展示了其在改进医疗对话生成方面的潜力。

英文摘要：

The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency. While previous studies have made progress in optimizing model performance for single-round medical Q&A tasks, there is a need to enhance the model's capability for multi-round conversations to avoid logical inconsistencies. To address this, we propose an approach called preference learning from process feedback~(PLPF), which integrates the doctor's diagnostic logic into LLMs. PLPF involves rule modeling, preference data generation, and preference alignment to train the model to adhere to the diagnostic process. Experimental results using Standardized Patient Testing show that PLPF enhances the diagnostic accuracy of the baseline model in medical conversations by 17.6%, outperforming traditional reinforcement learning from human feedback. Additionally, PLPF demonstrates effectiveness in both multi-round and single-round dialogue tasks, showcasing its potential for improving medical dialogue generation.

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。

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