arXiv 2501.094262025-01-16cs.CL

AutoCBT：一种用于心理咨询中认知行为疗法的自主多智能体框架

AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；University of Macau（澳门大学）；Shenzhen University（深圳大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））

Ancheng Xu, Di Yang, Renhao Li, Jingwei Zhu, Minghuan Tan, Min Yang, Wanxin Qiu, Mingchen Ma, Haihong Wu, Bingyu Li, Feng Sha, Chengming Li, Xiping Hu, Qiang Qu… 展开作者

Ancheng Xu, Di Yang, Renhao Li, Jingwei Zhu, Minghuan Tan, Min Yang, Wanxin Qiu, Mingchen Ma, Haihong Wu, Bingyu Li, Feng Sha, Chengming Li, Xiping Hu, Qiang Qu, Derek F. Wong, Ruifeng Xu

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AI总结：

针对现有基于大语言模型的CBT咨询系统智能体结构固定、回复质量不佳的问题，研究构建了引入动态路由与监督机制的AutoCBT自主多智能体框架，经双语数据集验证可提供更高质量的自动化心理咨询服务。

摘要：

传统线下心理咨询目前仍主要属于小众服务，通常是有心理问题的个体才会选择，而在线自动化咨询为那些因羞耻感而犹豫求助的人提供了一种潜在解决方案。认知行为疗法（Cognitive Behavioral Therapy, CBT）是心理咨询中一种核心且应用广泛的方法。大语言模型（Large Language Models, LLMs）与智能体技术的出现，使得自动化CBT诊断与治疗成为可能。然而，当前基于LLM的CBT系统所使用的智能体结构固定，限制了其自我优化能力，或是因响应模式冗余而给出空洞、无用的建议。在本研究中，我们利用类Quora（问答社区）和YiXinLi单轮咨询模型，构建了一个通用智能体框架，可在单轮心理咨询场景下生成高质量回复。我们使用一个双语数据集，评估了各框架生成的单轮咨询回复质量。随后，我们借鉴真实心理咨询的思路引入动态路由与监督机制，构建了一个面向CBT的自主多智能体框架，证明了其通用适用性。实验结果表明，AutoCBT能够提供质量更高的自动化心理咨询服务。

英文摘要：

Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential solution for those hesitant to seek help due to feelings of shame. Cognitive Behavioral Therapy (CBT) is an essential and widely used approach in psychological counseling. The advent of large language models (LLMs) and agent technology enables automatic CBT diagnosis and treatment. However, current LLM-based CBT systems use agents with a fixed structure, limiting their self-optimization capabilities, or providing hollow, unhelpful suggestions due to redundant response patterns. In this work, we utilize Quora-like and YiXinLi single-round consultation models to build a general agent framework that generates high-quality responses for single-turn psychological consultation scenarios. We use a bilingual dataset to evaluate the quality of single-response consultations generated by each framework. Then, we incorporate dynamic routing and supervisory mechanisms inspired by real psychological counseling to construct a CBT-oriented autonomous multi-agent framework, demonstrating its general applicability. Experimental results indicate that AutoCBT can provide higher-quality automated psychological counseling services.

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