@inproceedings{kim-etal-2026-phasemi,
    title = "{P}hase{MI}: A Motivational Interviewing Dataset for Enhancing Phase Progression in {LLM}-based Counseling",
    author = "Kim, Jina  and
      Jeon, Myeongho  and
      Cho, Soohyun  and
      Lim, Chae-Gyun  and
      Lim, Jongmin  and
      Min, Haewon  and
      Yang, Eunho",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1970/",
    pages = "39538--39570",
    ISBN = "979-8-89176-395-1",
    abstract = "The growing demand for scalable mental health support has increased interest in AI-based counseling systems grounded in Motivational Interviewing (MI). However, existing MI datasets do not explicitly model the structured progression of MI phases, which is essential for effective and goal-oriented counseling. To address this gap, we introduce PhaseMI, a phase-structured MI dataset, together with a data generation framework that employs therapist, client, and supervisor LLMs to explicitly control phase transitions. Compared to the best alternative baseline, PhaseMI achieves improved coverage of MI phases, with gains of 12.3{\%} in exploring, 37.6{\%} in guiding, and 61.1{\%} in choosing, and experimental evaluations demonstrate that it yields higher overall counseling quality than baseline datasets."
}