用剧本控制LLM对话,让AI心理咨询师行为可预测、可审查。
Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist"
- 用专家编写的治疗脚本约束LLM行为,确保对话符合治疗逻辑。
- 通过有限状态机实现对话路径可控,100次模拟对话验证可行性。
- 适合需要可解释性与安全性的临床级AI对话系统研发者。
基于大语言模型(LLM)的对话代理有望为用户提供可扩展的心理健康支持,甚至未来实现全规模的“AI心理治疗”。尽管这些代理已能进行流畅且主动的情感支持对话,但其内在缺乏两点关键能力:(a) 按预设规则一致可靠地行动以契合整体治疗理念;(b) 使决策路径可追溯,便于风险管理和临床评估——而这正是“AI心理治疗师”的基本要求。本文提出一种新型对话策略规划范式,使对话代理能够 (a) 依据专家撰写的“治疗脚本”执行动作,(b) 在对话过程中显式地在有限状态间转换。脚本作为确定性组件,以期望方式约束LLM行为,并建立了一个基础的AI心理治疗架构。我们采用不同提示技术实现了两种变体,并合成共100次由LLM模拟患者的对话。结果表明该技术具有可行性,并揭示了不同实现方式在效率与效果上的差异。
原文摘要 · Abstract (English)
Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support, and potentially even deliver full-scale "AI therapy'" in the future. While such agents can already conduct fluent and proactive emotional support conversations, they inherently lack the ability to (a) consistently and reliably act by predefined rules to align their conversation with an overarching therapeutic concept and (b) make their decision paths inspectable for risk management and clinical evaluation -- both essential requirements for an "AI Therapist". In this work, we introduce a novel paradigm for dialog policy planning in conversational agents enabling them to (a) act according to an expert-written "script" that outlines the therapeutic approach and (b) explicitly transition through a finite set of states over the course of the conversation. The script acts as a deterministic component, constraining the LLM's behavior in desirable ways and establishing a basic architecture for an AI Therapist. We implement two variants of Script-Based Dialog Policy Planning using different prompting techniques and synthesize a total of 100 conversations with LLM-simulated patients. The results demonstrate the feasibility of this new technology and provide insights into the efficiency and effectiveness of different implementation variants.
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