Graph2Counsel: 从客户端心理图谱生成临床导向的合成咨询对话
Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs
AI总结：
本文提出Graph2Counsel框架，通过客户端心理图谱生成合成咨询对话，提升数据真实性与质量，实验表明其在特定性、咨询师能力等方面优于现有数据集。
摘要：
随着心理健康支持需求的上升，使用大语言模型（LLMs）进行咨询的兴趣增加。然而，将LLMs适应到高风险安全关键领域受限于现实咨询数据的稀缺性。合成数据集提供了替代方案，但现有方法常依赖无结构或半结构化文本输入，忽视了客户端认知、情绪和行为状态之间的结构依赖性，常产生心理不一致的交互并降低数据真实性和质量。我们引入Graph2Counsel，一种基于客户端心理图谱（CPGs）生成合成咨询会话的框架，编码了客户端思想、情绪和行为之间的关系。Graph2Counsel采用由咨询师策略和CPGs引导的结构化提示管道，并探索了包括CoT（Wei等人，2022）和多代理反馈（Li等人，2025a）在内的提示策略。Graph2Counsel从76个CPGs中生成760个会话，涵盖多样化客户端资料。在专家评估中，我们的数据集在特定性、咨询师能力、真实性、对话流畅性和安全性方面优于现有数据集，具有显著的标注者间一致性（Krippendorff's α = 0.70）。在该数据集上微调开源模型在CounselingBench（Nguyen等人，2025）和CounselBench（Li等人，2025b）上表现提升，显示出下游应用价值。我们还公开了代码和数据。
英文摘要：
Rising demand for mental health support has increased interest in using Large Language Models (LLMs) for counseling, but adapting them to this safety-critical domain is hindered by limited real-world data due to privacy constraints. Synthetic datasets provide a promising alternative, but existing approaches often rely on unstructured or semi-structured text inputs and overlook structural dependencies between a client's cognitive, emotional, and behavioral states, leading to psychologically inconsistent and less realistic interactions. We introduce Graph2Counsel, a framework for generating synthetic counseling sessions grounded in Client Psychological Graphs (CPGs) that encode relationships among clients' thoughts, emotions, and behaviors. Graph2Counsel uses a structured prompting pipeline guided by counselor strategies and CPG, and explores prompting strategies including CoT and Multi-Agent Feedback. It produces 760 sessions from 76 CPGs across diverse client profiles. In expert evaluation, our dataset outperforms prior datasets on specificity, counselor competence, authenticity, conversational flow, and safety (Krippendorff's $α$ = 0.70). Fine-tuning an open-source model on this dataset improves performance on several metrics on CounselingBench and CounselBench, while matching baselines on others. We further introduce Therapy-Eval, a multi-turn evaluation framework, and demonstrate the effectiveness of our fine-tuned model in realistic therapeutic conversations. We make our code, data and fine-tuned model public.
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。
