DiaCBT：一个由认知概念化图引导的、用于基于CBT的心理咨询的长周期对话语料库
DiaCBT: A Long-Periodic Dialogue Corpus Guided by Cognitive Conceptualization Diagram for CBT-based Psychological Counseling
AI总结：
针对心理治疗可及性低、心理对话数据集匮乏的问题，本文构建了融入认知概念化图的长周期CBT咨询语料库DiaCBT，实验证明其能有效提升大语言模型的CBT咨询模拟能力，可用于训练更专业的咨询智能体。
摘要：
由于社会污名化以及治疗师资源有限，仅有一小部分受精神障碍困扰的人群能够获得心理治疗。具备专业心理治疗技能的大语言模型（LLM）为扩大心理健康服务的可及性提供了颇具前景的解决方案。然而，心理对话数据集的匮乏给开发有效的心理治疗引导型对话智能体带来了重大挑战。本文构建了一个基于认知行为疗法（CBT，Cognitive Behavioral Therapy）的长周期咨询对话语料库。我们整理的数据集包含每个咨询案例的多个会话，并纳入了认知概念化图（CCD，Cognitive Conceptualization Diagrams）以引导多种场景下的来访者模拟。为评估数据集的效用，我们训练了一个深度咨询模型，并提出了一套综合评估框架，依据已确立的CBT咨询心理标准对其进行基准测试。结果表明，DiaCBT有效提升了大语言模型模拟具备CBT专业能力的心理咨询师的能力，凸显了其在训练更专业的咨询智能体方面的潜力。
英文摘要：
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.
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。
