构建心理辅导对话数据集，提升大模型的认知行为治疗能力
DiaCBT: A Long-Periodic Dialogue Corpus Guided by Cognitive Conceptualization Diagram for CBT-based Psychological Counseling
- 基于认知概念图构建多轮心理辅导对话数据集
- 模型在认知行为治疗标准上表现显著优于基线
- 适合心理AI研发与临床评估研究者使用
由于社会偏见和治疗师资源有限，心理治疗仅覆盖少数有需求人群。大型语言模型（LLMs）若具备专业心理治疗能力，有望扩大心理健康服务可及性。然而，缺乏高质量的心理对话数据制约了相关智能体的发展。本文构建了一个基于认知行为疗法（CBT）的长周期对话语料库（DiaCBT），包含每名来访者多个会话，并引入认知概念化图（CCDs）指导模拟不同情境下的客户行为。为评估该数据集价值，我们训练了一个深度心理辅导模型，并提出一套涵盖心理治疗标准的综合评估框架。结果表明，DiaCBT能有效提升LLMs模拟专业心理咨询师的能力，验证其在训练更专业的心理辅导智能体方面的潜力。
原文摘要 · Abstract (English)
Psychotherapy reaches only a small fraction of individuals suffering from mental disorders due to social stigma and the limited availability of therapists. Large language models (LLMs), when equipped with professional psychotherapeutic skills, offer a promising solution to expand access to mental health services. However, the lack of psychological conversation datasets presents significant challenges in developing effective psychotherapy-guided conversational agents. In this paper, we construct a long-periodic dialogue corpus for counseling based on cognitive behavioral therapy (CBT). Our curated dataset includes multiple sessions for each counseling and incorporates cognitive conceptualization diagrams (CCDs) to guide client simulation across diverse scenarios. To evaluate the utility of our dataset, we train an in-depth counseling model and present a comprehensive evaluation framework to benchmark it against established psychological criteria for CBT-based counseling. Results demonstrate that DiaCBT effectively enhances LLMs' ability to emulate psychologists with CBT expertise, underscoring its potential for training more professional counseling agents.
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