抑郁症诊断对话模拟：具备三级记忆的自我改进精神科医生
Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory
AI总结：
针对抑郁障碍自动化诊断需求，提出具备三级记忆等结构的自我改进对话系统AMC，通过医患智能体模拟对话提升诊断准确率，可在少标注、不改LLM权重下实现领域适配。
摘要：
心理健康问题，尤其是抑郁障碍，在当代社会构成重大挑战，因此亟需开发有效的自动化诊断方法。本文提出Agent Mental Clinic（AMC，智能体心理诊所），这是一套自我改进的对话智能体系统，旨在通过患者与精神科医生智能体之间的模拟对话提升抑郁症诊断效果。为提升对话质量与诊断准确率，我们设计的精神科医生智能体包含三级记忆结构、充当“监督者”的对话控制与反思插件，以及记忆采样模块，充分发挥精神科医生智能体体现的技能，通过对话在抑郁风险与自杀风险诊断上取得了极高准确率。在真实场景采集的数据集上的实验结果表明，该系统模拟精神科医生的培训流程，在无需修改大语言模型（LLM）权重的情况下，即使仅能获得少量有代表性的标注案例，也能成为让LLM适配特定领域真实分布的一种颇具潜力的优化方法。
英文摘要：
Mental health issues, particularly depressive disorders, present significant challenges in contemporary society, necessitating the development of effective automated diagnostic methods. This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. To enhance the dialogue quality and diagnosis accuracy, we design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and reflect plugin that acts as ``supervisor'' and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation. Experiment results on datasets collected in real-life scenarios demonstrate that the system, simulating the procedure of training psychiatrists, can be a promising optimization method for aligning LLMs with real-life distribution in specific domains without modifying the weights of LLMs, even when only a few representative labeled cases are available.
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。
