发表机构
University of Toronto; Centre for Addiction and Mental Health（多伦多大学; 成瘾与心理健康中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对动机访谈的关键唤起任务，提出Evoke-Sim任务感知多阶段LLM模拟来访者框架，用于戒烟场景下MI咨询师评估，其评估效果优于现有模拟来访者，为相关评估设定了更高标准。
AI 中文摘要
基于大语言模型（LLM）的动机访谈（MI）咨询师的开发与基准测试如今常依赖基于LLM的模拟来访者。然而，过往关于模拟来访者的研究未与MI疗法的核心任务对齐，其中一项关键任务是唤起，即咨询师先引出来访者的矛盾心理，再强化其改变动机。本文提出Evoke-Sim，这是一种针对戒烟场景下MI咨询师评估的任务感知多阶段LLM来访者模拟框架，专为MI的唤起任务设计。Evoke-Sim采用结构化来访者档案、唤起任务特有的三阶段对话流程，以及用于调控各阶段可披露来访者档案信息的披露策略。研究表明，与现有基于档案的模拟来访者相比，Evoke-Sim在使用任务感知评估指标区分MI质量等级方面表现更优，同时减少了无依据的来访者表述与来访者信息的过早披露，为基于LLM的MI咨询师评估设定了更高标准。
英文摘要
The development and benchmarking of Large Language Model (LLM)-based Motivational Interviewing (MI) counsellors now often rely on LLM-based simulated clients. Prior work on simulated clients, however, has not aligned with the specific tasks fundamental to the MI therapy approach. A key task is evoking, in which the counsellor first elicits the client's ambivalence and then strengthens the client's motivation for change. We present Evoke-Sim, a task-aware, multi-stage LLM-based client simulation framework for evaluating MI counsellors in smoking cessation, designed specifically for the evoking MI task. Evoke-Sim employs structured client profiles, an evoking-specific three-stage conversation flow, and a reveal policy that regulates which client profile information might be disclosed at each stage. We show that compared to existing profile-grounded simulated clients, Evoke-Sim is better at differentiating levels of MI quality using task-aware evaluation metrics, while reducing non-grounded client statements and premature disclosure of client information, setting a higher standard for the evaluation of LLM-based MI counsellors.
Comments53 pages
