CAMI用AI模拟心理咨询师，帮用户克服矛盾心理、推动改变
CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration
- 基于状态推断与话题探索的三模块框架，模仿专业心理咨询流程
- 在模拟对话中表现优于现有方法，能准确识别用户状态并引导改变谈话
- 适合需要低成本、可扩展心理支持的人群，尤其关注行为改变场景
对话式心理咨询助手正成为应对心理健康服务需求增长的重要工具。本文提出CAMI，一种基于动机访谈（Motivational Interviewing, MI）的自动化咨询代理，该方法以客户为中心，旨在缓解矛盾心理并促进行为改变。CAMI采用创新的STAR框架，包含客户状态推断、动机话题探索和回应生成三个模块，依托大语言模型（LLMs）实现。各模块协同工作，有效激发改变谈话（change talk），符合MI原则，并提升来自不同背景用户的咨询效果。通过自动化与人工评估结合，使用模拟客户测试其在MI技能水平、状态推断准确性、话题探索能力及整体咨询成功率方面的表现。结果表明，CAMI不仅超越多个先进方法，且表现出更贴近真实咨询师的行为特征。消融实验进一步验证了状态推断与话题探索在性能提升中的关键作用。
原文摘要 · Abstract (English)
Conversational counselor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI) -- a client-centered counseling approach designed to address ambivalence and facilitate behavior change. CAMI employs a novel STAR framework, consisting of client's state inference, motivation topic exploration, and response generation modules, leveraging large language models (LLMs). These components work together to evoke change talk, aligning with MI principles and improving counseling outcomes for clients from diverse backgrounds. We evaluate CAMI's performance through both automated and manual evaluations, utilizing simulated clients to assess MI skill competency, client's state inference accuracy, topic exploration proficiency, and overall counseling success. Results show that CAMI not only outperforms several state-of-the-art methods but also shows more realistic counselor-like behavior. Additionally, our ablation study underscores the critical roles of state inference and topic exploration in achieving this performance.
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