Cactus：基于认知行为理论的心理咨询对话研究
Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory
AI总结：
针对心理咨询开源LLM训练缺乏真实数据集的问题，提出基于认知行为疗法的多轮对话数据集Cactus，经实验验证用其训练的Camel模型咨询技能更优，相关资源已公开。
摘要：
近年来，随着越来越多的人开始关注自身心理健康，心理咨询的需求显著增长。这一需求激增推动了利用大语言模型（LLMs）担任咨询师以提升咨询可及性的相关研究。为保障来访者隐私，开源LLM的训练面临一项关键挑战：缺乏真实的咨询数据集。针对这一问题，我们提出了Cactus，这是一个多轮对话数据集，采用认知行为疗法（CBT）的目标导向结构化方法模拟真实咨询互动。我们通过设计具有多样、特定人设的来访者，并让咨询师在互动中系统应用CBT技术，构建出多样化且贴近真实场景的数据集。为评估数据质量，我们采用用于评估真实咨询疗程的成熟心理学标准进行基准测试，确保结果与专家评估相一致。实验结果表明，使用Cactus训练的模型Camel在咨询技能方面优于其他模型，凸显了其作为咨询智能体的有效性与潜力。我们已将数据集、模型和代码公开。
英文摘要：
Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. This surge has accelerated efforts to improve the accessibility of counseling by using large language models (LLMs) as counselors. To ensure client privacy, training open-source LLMs faces a key challenge: the absence of realistic counseling datasets. To address this, we introduce Cactus, a multi-turn dialogue dataset that emulates real-life interactions using the goal-oriented and structured approach of Cognitive Behavioral Therapy (CBT). We create a diverse and realistic dataset by designing clients with varied, specific personas, and having counselors systematically apply CBT techniques in their interactions. To assess the quality of our data, we benchmark against established psychological criteria used to evaluate real counseling sessions, ensuring alignment with expert evaluations. Experimental results demonstrate that Camel, a model trained with Cactus, outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent. We make our data, model, and code publicly available.
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。
