超越共情：融合诊断与治疗推理的大语言模型心理健康咨询
Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling
He Hu, Juzheng Si, Qianning Wang, Fei Ma, Fuji Ren, Sicheng Zhao, Yucheng Zhou, Laizhong Cui
AI 速读 基于摘要
作者提出 PsyLLM，通过自动化数据合成将真实心理困扰帖子转化为融合 DSM/ICD 诊断标准与多种治疗框架的多轮咨询对话及推理轨迹，并构建四维评测基准。
cs.CL
查看原文摘要
Large language models (LLMs) hold significant potential for psychological support, capable of generating empathetic responses and simulating therapeutic conversations. However, existing LLM-based approaches often lack sufficient grounding in structured diagnostic reasoning and diverse therapeutic strategies, limiting their effectiveness in complex counseling scenarios. To address these limitations, we propose PsyLLM, the first large language model designed to incorporate diagnostic- and therapeutic-oriented reasoning for mental health counseling. To develop PsyLLM, we design a novel automated data synthesis pipeline that processes real-world mental health posts collected from Reddit, where users frequently express psychological distress and seek community support. The pipeline transforms these posts into multi-turn counseling dialogues and leverages LLMs guided by international diagnostic standards (e.g., DSM/ICD) and multiple therapeutic frameworks (e.g., CBT, ACT, psychodynamic) to generate structured, clinically informed reasoning traces. A rigorous multi-dimensional filtering mechanism further improves the quality and clinical fidelity of the synthesized dialogue data. In addition, we introduce a new benchmark and evaluation protocol, assessing counseling performance across four key dimensions. Our experiments, along with evaluations by professional counseling experts, demonstrate that PsyLLM consistently outperforms various baseline models on this benchmark. Code, data, and models are available at https://github.com/Emo-gml/PsyLLM.