arXiv 2505.157152025-11-03cs.CL

超越共情：将诊断与治疗推理集成到大语言模型中以用于心理健康咨询

Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling

College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济发展实验室）；SKL-IOTSC, CIS, University of Macau（澳门科学馆物联网与智慧城市研究中心）；Shandong University（山东大学）；Auckland University of Technology（技术大学（奥克兰））；University of Electronic Science and Technology of China（电子科技大学）

He Hu, Yucheng Zhou, Juzheng Si, Qianning Wang, Hengheng Zhang, Fuji Ren, Fei Ma, Laizhong Cui, Qi Tian

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对现有LLM心理咨询缺乏临床诊断推理与多元治疗策略的问题，提出首个系统整合诊断与治疗推理的模型PsyLLM，通过自动化数据合成流水线生成临床对齐数据，并在四个维度的新基准上显著超越基线模型。

摘要：

大语言模型（LLMs）在心理健康支持方面具有重要潜力，能够生成共情回复并模拟治疗性对话。然而，现有的基于LLM的方法往往缺乏现实心理咨询所需的临床基础，特别是在符合DSM/ICD等标准的明确诊断推理，以及超越基本共情或单一策略的多样化治疗模式方面。为了解决这些关键局限，我们提出了PsyLLM，这是首个设计用于系统整合心理健康咨询中诊断与治疗推理的大语言模型。为了开发PsyLLM，我们设计了一条新颖的自动化数据合成流水线，处理从Reddit收集的真实心理健康帖子，用户经常在该平台分享心理困扰并寻求社区支持。该流水线处理真实世界的心理健康帖子，生成多轮对话结构，并利用由国际诊断标准（如DSM/ICD）和多种治疗框架（如CBT、ACT、心理动力学）引导的LLMs来模拟详细的临床推理过程。严格的多维过滤确保了高质量、临床对齐对话数据的生成。此外，我们引入了一个新的基准和评估协议，从四个关键维度评估咨询质量。我们的实验表明，PsyLLM在该基准上显著优于最先进的基线模型。模型权重和数据集已在 https://github.com/Emo-gml/PsyLLM 公开发布。

英文摘要：

Large language models (LLMs) hold significant potential for mental health support, capable of generating empathetic responses and simulating therapeutic conversations. However, existing LLM-based approaches often lack the clinical grounding necessary for real-world psychological counseling, particularly in explicit diagnostic reasoning aligned with standards like the DSM/ICD and incorporating diverse therapeutic modalities beyond basic empathy or single strategies. To address these critical limitations, we propose PsyLLM, the first large language model designed to systematically integrate both diagnostic and therapeutic 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 share psychological distress and seek community support. This pipeline processes real-world mental health posts, generates multi-turn dialogue structures, and leverages LLMs guided by international diagnostic standards (e.g., DSM/ICD) and multiple therapeutic frameworks (e.g., CBT, ACT, psychodynamic) to simulate detailed clinical reasoning processes. Rigorous multi-dimensional filtering ensures the generation of high-quality, clinically aligned dialogue data. In addition, we introduce a new benchmark and evaluation protocol, assessing counseling quality across four key dimensions. Our experiments demonstrate that PsyLLM significantly outperforms state-of-the-art baseline models on this benchmark. The model weights and dataset have been publicly released at https://github.com/Emo-gml/PsyLLM.

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。