@inproceedings{yang-etal-2025-cami,
    title = "{CAMI}: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration",
    author = "Yang, Yizhe  and
      Achananuparp, Palakorn  and
      Huang, Heyan  and
      Jiang, Jing  and
      Kit, Phey Ling  and
      Lim, Nicholas Gabriel  and
      Ern, Cameron Tan Shi  and
      Lim, Ee-Peng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1024/",
    pages = "21037--21081",
    ISBN = "979-8-89176-251-0",
    abstract = "Conversational counselor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI) {--} a client-centered counseling approach designed to address ambivalence and facilitate behavior change. CAMI employs a novel STAR framework, consisting of client{'}s state inference, motivation topic exploration, and response generation modules, leveraging large language models (LLMs). These components work together to evoke change talk, aligning with MI principles and improving counseling outcomes for diverse clients. We evaluate CAMI{'}s performance through both automated and expert evaluations, utilizing simulated clients to assess MI skill competency, client{'}s state inference accuracy, topic exploration proficiency, and overall counseling success. Results show that CAMI not only outperforms several state-of-the-art methods but also shows more realistic counselor-like behavior. Additionally, our ablation study underscores the critical roles of state inference and topic exploration in achieving this performance."
}