  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.
