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.

通过设计具有不同、具体人设和在交互中系统应用认知行为疗法技术的辅导员，我们使用目标导向和结构化方法创建了Cactus多轮对话数据集，该数据集模拟了现实生活中的互动，以解决使用大型语言模型作为辅导员时面临的真实辅导数据集缺失的挑战。实验结果表明，利用Cactus训练的Camel模型在辅导技能上优于其他模型，突显出它作为辅导代理人的有效性和潜力。我们公开提供了数据、模型和代码。

仙人掌：基于认知行为理论的心理咨询对话