Papers with Motivational Interviewing
Modeling Temporality of Human Intentions by Domain Adaptation (D18-1)
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| Challenge: | Recent research shows that themes and words within a conversation change across time, whereas topics and the patient's attitude towards their willingness to change might shift. |
| Approach: | They propose a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). |
| Outcome: | The proposed method improves on a college alcoholism dataset using a bi-LSTM and topic model to learn language usage change across different time sessions. |
How Well Can Large Language Models Reflect? A Human Evaluation of LLM-generated Reflections for Motivational Interviewing Dialogues (2025.coling-main)
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Erkan Basar, Xin Sun, Iris Hendrickx, Jan de Wit, Tibor Bosse, Gert-Jan De Bruijn, Jos A. Bosch, Emiel Krahmer
| Challenge: | Motivational Interviewing (MI) is a counseling technique that promotes behavioral change through reflective responses to mirror or refine client statements. |
| Approach: | They assess the potential of Large Language Models (LLMs) to generate MI reflections via three LLMs: GPT-4, Llama-2, and BLOOM. |
| Outcome: | The proposed models generate meaningful reflections comparable to human therapists, but significant challenges remain. |
MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling (2026.findings-acl)
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| Challenge: | Existing reasoning large language models (LLMs) generate responses without explicitly aligning thoughts with counseling techniques, limiting their effectiveness. |
| Approach: | They propose a lightweight thinking model that generates therapeutic thoughts to guide MI counseling agents in strategy selection and response generation. |
| Outcome: | The proposed model achieves theory-of-mind assessment comparable to state-of the-art systems with an order of magnitude less computation. |
Beyond Words: Decoding Facial Expression Dynamics in Motivational Interviewing (2024.lrec-main)
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| Challenge: | Motivational Interviewing (MI) is a directive, clientcentered therapeutic approach designed to facilitate behaviour change by enhancing individuals' intrinsic motivation. |
| Approach: | They propose to decode facial expressions of both client and therapist in the context of Motivational Interviewing using an annotation system. |
| Outcome: | The proposed method identifies facial expressions of both interlocutors and client over a counseling session and reveals the correlation between the facial expression and the different types of talk, as well as the interplay between interlocutetors’ expressions. |
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy (2025.naacl-long)
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| Challenge: | Motivational Interviewing (MI) is gaining attention as a theoretical basis for mental health chatbots. |
| Approach: | They propose a framework that simulates MI sessions enriched with the expertise of professional therapists by using large language models to generate utterances through prompt engineering. |
| Outcome: | The proposed framework simulates MI sessions enriched with the expertise of professional therapists and employs large language models to generate utterances through prompt engineering. |
Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes (P19-1)
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| Challenge: | a new study examines the role of dialogue observers in psychotherapy . the model is based on motivational interviewing, which is effective for treating addictions . |
| Approach: | They propose to model MI behavioral codes for therapists by an observer . they propose to use the observer to forecast therapist and client MI behavioral code . |
| Outcome: | The proposed model outperforms baseline models for both tasks and reveals tradeoffs in performance. |
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning (2024.findings-acl)
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Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley
| Challenge: | Motivational Interviewing (MI) requires a system that can infer how to motivate users to adopt positive lifestyle changes. |
| Approach: | They propose a framework that can learn and apply conversation strategies from expert demonstrations by using natural language inductive rules. |
| Outcome: | The proposed framework outperforms in-context demonstrations that are over 50 times longer and can learn natural language strategies from demonstrations. |
CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration (2025.acl-long)
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Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Tan Shi Ern, Ee-Peng Lim
| Challenge: | Motivational Interviewing (MI) is a client-centered counseling technique designed to address ambivalence and facilitate behavior change in clients. |
| Approach: | They propose to use a STAR framework to evoke change talk by using large language models to assess MI skill competency, client’s state inference accuracy, topic exploration proficiency, and overall counseling success. |
| Outcome: | The proposed agent outperforms several state-of-the-art methods and shows more realistic counselor-like behavior. |
A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit (2025.findings-acl)
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Zafarullah Mahmood, Soliman Ali, Jiading Zhu, Mohamed Abdelwahab, Michelle Yu Collins, Sihan Chen, Yi Cheng Zhao, Jodi Wolff, Osnat C. Melamed, Nadia Minian, Marta Maslej, Carolynne Cooper, Matt Ratto, Peter Selby, Jonathan Rose
| Challenge: | Large language models (LLMs) are being used to provide automated talk therapy . however, it is crucial to know if they would be effective and adhere to known standards. |
| Approach: | They propose to use large language models to automate talk therapy with a focus on tobacco addiction. |
| Outcome: | The proposed chatbot showed adherence to MI standards in 98% of utterances, higher than human counsellors. |
PhaseMI: A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM-based Counseling (2026.findings-acl)
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| Challenge: | Existing MI datasets do not explicitly model structured progression of MI phases, which is essential for effective and goal-oriented counseling. |
| Approach: | They propose a phase-structured MI dataset with a data generation framework that employs therapist, client, and supervisor LLMs to explicitly control phase transitions. |
| Outcome: | The proposed model achieves 12.3% better coverage of MI phases, 37.6% in guiding, and 61.1% in choosing. |
Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems (2026.findings-acl)
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| Challenge: | Motivational interviewing (MI) is a goal-directed dialogue aimed at motivating clients to change their behavior. |
| Approach: | They propose a method for updating multi-frame dialogue states and a strategy decision mechanism that dynamically determines the response focus in a manner grounded in MI principles. |
| Outcome: | The proposed method generates responses aligned with MI principles and frequently asks questions to elicit change talk. |