PsychēChat: 一种专注于心理辅导中情感转变跟踪和安全风险分析的共情框架
PsychēChat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling
AI总结：
PsychēChat通过情感管理与风险控制模块，提升心理辅导中情感转变跟踪和安全风险分析的能力。
摘要：
大型语言模型（LLMs）在心理辅导领域展现了显著的进步。然而，现有模型通常没有显式建模咨询者在咨询过程中的情感转变，这是经典心理学学派的核心关注点。此外，如何将咨询师模型的回应与这些情感转变对齐，同时主动缓解安全风险仍属探索领域。为填补这些空白，我们提出了PsychēChat，该框架明确整合了心理辅导中的情感转变跟踪和安全风险分析。具体而言，我们采用互动角色扮演来合成咨询师-咨询者对话，包含两个模块：情感管理模块，用于捕捉咨询者的当前情绪和情感转变；以及风险控制模块，用于预测咨询者的后续反应并识别潜在风险。此外，我们引入了两种建模范式。Agent模式将情感管理、风险控制和咨询师回应构建成一个协作的多代理流水线。LLM模式将这些阶段整合到统一的思考链中进行端到端推理，平衡效率和性能。广泛的实验，包括互动评分、对话层面评估和人类评估，表明PsychēChat在情感洞察和安全控制方面优于现有方法。
英文摘要：
Large language models (LLMs) have demonstrated notable advancements in psychological counseling. However, existing models generally do not explicitly model seekers' emotion shifts across counseling sessions, a core focus in classical psychological schools. Moreover, how to align counselor models' responses with these emotion shifts while proactively mitigating safety risks remains underexplored. To bridge these gaps, we propose PsychēChat, which explicitly integrates emotion shift tracking and safety risk analysis for psychological counseling. Specifically, we employ interactive role-playing to synthesize counselor--seeker dialogues, incorporating two modules: Emotion Management Module, to capture seekers' current emotions and emotion shifts; and Risk Control Module, to anticipate seekers' subsequent reactions and identify potential risks. Furthermore, we introduce two modeling paradigms. The Agent Mode structures emotion management, risk control, and counselor responses into a collaborative multi-agent pipeline. The LLM Mode integrates these stages into a unified chain-of-thought for end-to-end inference, balancing efficiency and performance. Extensive experiments, including interactive scoring, dialogue-level evaluation, and human assessment, demonstrate that PsychēChat outperforms existing methods for emotional insight and safety control.
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。
