Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection. Recent work focuses on combining task-oriented dialogue and chitchat to simulate the interview-based depression diagnosis. Whereas, these methods can not well capture the changing information, feelings, or symptoms of the patient during dialogues. Moreover, no explicit framework has been explored to guide the dialogue, which results in some useless communications that affect the experience. In this paper, we propose to integrate Psychological State Tracking (POST) within the large language model (LLM) to explicitly guide depression-diagnosis-oriented chat. Specifically, the state is adapted from a psychological theoretical model, which consists of four components, namely Stage, Information, Summary and Next. We fine-tune an LLM model to generate the dynamic psychological state, which is further used to assist response generation at each turn to simulate the psychiatrist. Experimental results on the existing benchmark show that our proposed method boosts the performance of all subtasks in depression-diagnosis-oriented chat.

翻译：抑郁症诊断导向的聊天旨在引导患者自我表达，以收集用于抑郁症检测的关键症状。近期研究聚焦于结合任务导向对话和闲聊，模拟基于访谈的抑郁症诊断过程。然而，这些方法难以充分捕捉对话过程中患者不断变化的信息、情绪或症状。此外，尚缺乏明确的框架来指导对话，导致出现一些影响体验的无用交流。本文提出将心理状态追踪（POST）集成到大语言模型（LLM）中，以明确指导抑郁症诊断导向的聊天。具体而言，该状态源于心理学理论模型，包含四个组件：阶段、信息、总结和下一步。我们微调大语言模型以生成动态心理状态，并利用该状态辅助每一轮对话中的响应生成，模拟精神科医生的行为。在现有基准数据集上的实验结果表明，我们提出的方法提升了抑郁症诊断导向聊天中所有子任务的性能。