@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T PhaseMI: A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM-based Counseling
%A Kim, Jina
%A Jeon, Myeongho
%A Cho, Soohyun
%A Lim, Chae-Gyun
%A Lim, Jongmin
%A Min, Haewon
%A Yang, Eunho
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F kim-etal-2026-phasemi
%X 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.
%U https://aclanthology.org/2026.findings-acl.1970/
%P 39538-39570
Markdown (Informal)
[PhaseMI: A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM-based Counseling](https://aclanthology.org/2026.findings-acl.1970/) (Kim et al., Findings 2026)
ACL
- Jina Kim, Myeongho Jeon, Soohyun Cho, Chae-Gyun Lim, Jongmin Lim, Haewon Min, and Eunho Yang. 2026. PhaseMI: A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM-based Counseling. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39538–39570, San Diego, California, United States. Association for Computational Linguistics.