StoryMI: 可控的多智能体治疗性对话生成
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation
AI总结：
提出StoryMI框架，通过多LLM智能体协作、情境故事基础和动态策略控制，生成符合动机性访谈标准的治疗性对话，并构建评估协议和数据集验证其有效性。
摘要：
大型语言模型（LLM）可以生成流畅的对话，但先前的工作缺乏情境基础、动态策略控制以及与动机性访谈（MI）临床标准对齐的评估。我们引入了StoryMI，一个用于可控MI对话生成的多LLM智能体框架，其中基于问卷的客户档案被扩展为情境故事，为对话提供叙事背景。治疗师和客户智能体生成由交互智能体选择的MI代码引导的MI编码话语，而交互智能体动态协调交换以在多次轮对话中控制MI策略。我们提出了一个两级评估协议：词汇指标和宏观层面咨询策略的MI特定度量，以及LLM作为评判者和人类专家评估。我们构建了一个包含6K模拟MI对话的数据集，基于1K问卷-故事对，涵盖12个MI代码和13个症状领域，并对六个开源和闭源LLM进行了基准测试。我们的结果表明，情境基础和宏观层面控制可以提高MI依从性和临床合理性，展示了结构化多智能体工作流在心理治疗对话生成中的有效性。我们提供代码和数据以促进可重复性。
英文摘要：
Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligned with clinical standards in motivational interviewing (MI). We introduce StoryMI, a multi-LLM agent framework for controllable MI dialogue generation, where questionnaire-based client profiles are expanded into situational stories that provide narrative context for the dialogue. Therapist and client agents generate MI-coded utterances guided by MI codes selected by the interaction agent, while an interaction agent dynamically coordinates exchanges to control MI strategies during a multi-turn conversation. We propose a two-level evaluation protocol: lexical metrics and MI-specific measures of macro-level counseling strategies, alongside LLM-as-judge and human expert assessments. We construct a dataset of 6K simulated MI dialogues grounded in 1K questionnaire-story pairs, covering 12 MI codes and 13 symptom domains, and benchmark six open- and closed-source LLMs. Our results show that situational grounding and macro-level control can improve MI adherence and clinical plausibility, demonstrating the effectiveness of a structured multi-agent workflow for psychotherapy dialogue generation. We provide code and data for reproducibility.
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。
