Toward expert-level motivational interviewing for health behavior improvement with LLMs

• URL: http://arxiv.org/abs/2512.15446v1
• Date: Wed, 17 Dec 2025 13:43:26 GMT
• Title: Toward expert-level motivational interviewing for health behavior improvement with LLMs
• Authors: Run-ze Hu, Yang Yang, Yi-hang Yang, Jing-qi Kong, Jia-hui Luo, Wen-yu Yang, Jing Chen, Jing-yao Liu, Hui-qun Zeng, Lei Zhang, Zheng Liu,
• Abstract summary: Motivational interviewing (MI) is an effective counseling approach for promoting health behavior change, but its impact is constrained by the need for highly trained human counselors.<n>This study developed and evaluated Large Language Models for Motivational Interviewing (MI-LLMs)<n>Three Chinese-capable open-source LLMs were fine-tuned on this corpus and were named as MI-LLMs.
• Score: 17.267453197266715
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: Motivational interviewing (MI) is an effective counseling approach for promoting health behavior change, but its impact is constrained by the need for highly trained human counselors. Objective: This study aimed to explore a scalable alternative by developing and evaluating Large Language Models for Motivational Interviewing (MI-LLMs). Methods: We first curated five Chinese psychological counseling corpora and, using GPT-4 with an MI-informed prompt, transcribed multi-turn dialogues from the two highest-quality datasets (CPsyCounD and PsyDTCorpus) into 2,040 MI-style counseling conversations, of which 2,000 were used for training and 40 for testing. Three Chinese-capable open-source LLMs (Baichuan2-7B-Chat, ChatGLM-4-9B-Chat and Llama-3-8B-Chinese-Chat-v2) were fine-tuned on this corpus and were named as MI-LLMs. We evaluated MI-LLMs using round-based automatic metrics and expert manual coding with the Motivational Interviewing Treatment Integrity (MITI) Coding Manual 4.2.1. Results: Across all three models, fine-tuning substantially improved BLEU-4 and ROUGE scores compared with the base models, and manual coding showed that MI-LLMs achieved technical and relational global scores, and MI-adherent ratios that approached those of real MI dialogues, although complex reflections and reflection-to-question ratios remained less frequent. Conclusions: These findings provide initial evidence that MI-oriented fine-tuning can endow general-purpose LLMs with core MI-consistent counseling behaviors, suggesting a scalable pathway toward AI-assisted health behavior change support while underscoring the need for further work on data scale, complex MI skills and real-world intervention trials.

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