迈向利用大语言模型的专家级激励访谈以改善健康行为
Toward expert-level motivational interviewing for health behavior improvement with LLMs
AI总结：
本研究通过开发和评估基于大语言模型的激励访谈系统，探索了利用AI促进健康行为改变的可行性，展示了微调后模型在激励访谈技术上的表现。
摘要：
背景：激励访谈（MI）是一种促进健康行为改变的有效咨询方法，但其影响受到需要高度培训的人类顾问的限制。目标：本研究旨在探索一种可扩展的替代方案，通过开发和评估用于激励访谈的大型语言模型（MI-LLMs）。方法：我们首先整理了五个中文心理咨询服务语料库，并使用GPT-4配合激励访谈提示，将两个质量最高的数据集（CPsyCounD和PsyDTCorpus）的多轮对话转录成2040个激励访谈风格的咨询对话，其中2000个用于训练，40个用于测试。三个中文能力的开源LLMs（Baichuan2-7B-Chat、ChatGLM-4-9B-Chat和Llama-3-8B-Chinese-Chat-v2）在该语料库上进行微调，并被命名为MI-LLMs。我们使用基于轮次的自动指标和专家手动编码（使用激励访谈治疗完整性（MITI）编码手册4.2.1）对MI-LLMs进行了评估。结果：在所有三个模型中，微调显著提高了BLEU-4和ROUGE得分，与基模型相比。手动编码显示，MI-LLMs在技术性和关系性全球得分以及MI符合率方面接近真实MI对话，尽管复杂反思和反思-问题比率仍较少。结论：这些发现提供了初步证据，表明面向MI的微调可以赋予通用LLMs核心MI一致的咨询行为，表明了通往AI辅助健康行为改变支持的可扩展路径，同时突显了进一步工作的重要性，包括数据规模、复杂MI技能和真实世界干预试验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
