Challenge: Motivational interviewing (MI) is a client-centered counseling technique that encourages individuals to change behaviors through emphatic conversations.
Approach: They propose to use large language models to generate more controllable dialogues with explainability by prompting LLMs to predict appropriate strategies as reasoning and utilizing these strategies to guide dialogue generation.
Outcome: The proposed model generates more controllable and explainable dialogues with a set of MI skills.

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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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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.
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)

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Challenge: Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change.
Approach: They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes .
Outcome: The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation.
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.
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.
Eliciting Motivational Interviewing Skill Codes in Psychotherapy with LLMs: A Bilingual Dataset and Analytical Study (2024.lrec-main)

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Challenge: Motivational interviewing (MI) is an essential, directive, client-centered counseling technique.
Approach: They propose a bilingual dataset of MI conversations in English and Dutch . they propose an approach to elicit MISC expertise from Large language models .
Outcome: The proposed approach yields results aligned with expert annotations and maintains consistent performance across languages.
Aligning Large Language Models for Controllable Recommendations (2024.acl-long)

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Challenge: Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template.
Approach: They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions.
Outcome: The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy.
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions (2025.findings-acl)

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Challenge: Large language models (LLMs) can handle extensive context and multi-turn reasoning.
Approach: They propose a taxonomy dividing psychotherapy into stages of assessment, diagnosis, and treatment to examine LLM advancements and challenges.
Outcome: The proposed taxonomy reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration.
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.
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.
Motivational Interviewing Transcripts Annotated with Global Scores (2024.lrec-main)

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Challenge: Motivational interviewing (MI) is a counseling approach that aims to increase intrinsic motivation and commitment to change.
Approach: They propose to annotate MI therapy sessions written in English from public sources . they explore the potential use of the dataset for training MI language models .
Outcome: The proposed dataset includes 242 MI demonstration transcripts annotated with therapist behavioral codes and global scores and client language EAsy Rating (CLEAR) tags for client speech.

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