@inproceedings{zhang-etal-2025-cbt,
    title = "{CBT}-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy",
    author = "Zhang, Mian  and
      Yang, Xianjun  and
      Zhang, Xinlu  and
      Labrum, Travis  and
      Chiu, Jamie C.  and
      Eack, Shaun M.  and
      Fang, Fei  and
      Wang, William Yang  and
      Chen, Zhiyu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-long.196/",
    doi = "10.18653/v1/2025.naacl-long.196",
    pages = "3864--3900",
    ISBN = "979-8-89176-189-6",
    abstract = "There is a significant gap between patient needs and available mental health support today. In this paper, we aim to thoroughly examine the potential of using Large Language Models (LLMs) to assist professional psychotherapy. To this end, we propose a new benchmark, CBT-Bench, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We include three levels of tasks in CBT-Bench: **I: Basic CBT knowledge acquisition**, with the task of multiple-choice questions; **II: Cognitive model understanding**, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; **III: Therapeutic response generation**, with the task of generating responses to patient speech in CBT therapy sessions.These tasks encompass key aspects of CBT that could potentially be enhanced through AI assistance, while also outlining a hierarchy of capability requirements, ranging from basic knowledge recitation to engaging in real therapeutic conversations. We evaluated representative LLMs on our benchmark. Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios requiring deep analysis of patients' cognitive structures and generating effective responses, suggesting potential future work."
}