MAGneT: 协调多智能体生成合成多轮心理健康咨询会话
MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions
AI总结：
MAGneT通过协调多智能体生成合成多轮心理健康咨询会话，提升咨询质量和评估一致性。
摘要：
随着对可扩展心理咨询服务的需求增长，高质量且符合隐私要求的数据需求日益凸显，但此类数据仍然稀缺。本文介绍MAGneT，一种新颖的多智能体框架，用于合成心理咨询服务生成，该框架将咨询师回应生成分解为由专门的LLM智能体处理的协调子任务，每个智能体建模一种关键的心理技术。与以往单智能体方法不同，MAGneT更能捕捉真实咨询的结构和细微差别。我们进一步提出一个统一的评估框架，整合了多样化的自动指标，并将专家评估从四个扩展到九个咨询维度，从而解决以往评估协议中的一致性问题。从经验上，MAGneT显著优于现有方法：专家在77.2%的情况下更喜欢MAGneT生成的会话，且MAGneT生成的会话在认知疗法评分量表（CTRS）上表现出3.2%更高的总体咨询技能和4.3%更高的CBT特定技能。一个在MAGneT生成数据上微调的开源Llama3-8B-Instruct模型在CTRS上平均比使用基线合成数据集微调的模型高出6.9%。我们还公开了我们的代码和数据。
英文摘要：
The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains scarce. Here we introduce MAGneT, a novel multi-agent framework for synthetic psychological counseling session generation that decomposes counselor response generation into coordinated sub-tasks handled by specialized LLM agents, each modeling a key psychological technique. Unlike prior single-agent approaches, MAGneT better captures the structure and nuance of real counseling. We further propose a unified evaluation framework that consolidates diverse automatic metrics and expands expert assessment from four to nine counseling aspects, thus addressing inconsistencies in prior evaluation protocols. Empirically, MAGneT substantially outperforms existing methods: experts prefer MAGneT-generated sessions in 77.2% of cases on average across the nine aspects over the strongest baseline, and sessions generated by MAGneT using Llama3-8B-Instruct backbone yield 3.2% higher general counseling skills and 4.3% higher CBT-specific skills on cognitive therapy rating scale (CTRS). An open source Llama3-8B-Instruct model fine-tuned on MAGneT-generated data also outperforms models fine-tuned using baseline synthetic datasets by 6.9% on average on CTRS. We make our code, data and fine-tuned model public.
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。
