EFT-CoT：一种针对情感导向治疗的多智能体链式思考框架
EFT-CoT: A Multi-Agent Chain-of-Thought Framework for Emotion-Focused Therapy
AI总结：
EFT-CoT提出一种基于情感导向治疗的多智能体框架，通过三个阶段工作流程提升心理健康问答的共情深度和专业性。
摘要：
利用大型语言模型（LLMs）进行心理健康问答（MHQA）为缓解心理健康资源短缺提供了一种有前景的方法。然而，先前的工作主要依赖于认知行为疗法（CBT），并主要采用以理性认知重构为中心的自上而下策略，对身体体验和基本情绪处理的支持有限。为解决这一差距，我们提出了EFT-CoT，一种基于情感导向治疗（EFT）的多智能体链式思考框架。EFT-CoT将干预定义为三个阶段的工作流程：身体感知、认知探索和叙述干预。该框架使用八个专门的智能体来建模关键过程，包括躯体意识映射、适应性评估、核心信念提取和叙述重构。基于此框架，我们构建了EFT-Instruct，一个高质量的指令微调数据集，该数据集从约67,000篇真实的求助文本的进程级增强中构建而成，并进一步微调了一个专用模型EFT-LLM。实验表明，EFT-LLM在共情深度和结构专业性方面始终优于强大的基线和人类响应。消融研究进一步验证了关键机制的贡献，而白盒审计展示了关键中间状态的一致性和可追溯性。总体而言，这项工作提供了一个可重复的框架-数据-模型管道，用于将EFT机制嵌入基于LLM的心理健康支持中。
英文摘要：
The use of large language models (LLMs) for Mental Health Question Answering (MHQA) offers a promising way to alleviate shortages in mental health resources. However, prior work has mainly relied on Cognitive Behavioral Therapy (CBT) and predominantly follows a top-down strategy centered on rational cognitive restructuring, providing limited support for embodied experience and primary emotion processing. To address this gap, we propose EFT-CoT, a multi-agent chain-of-thought framework grounded in Emotion-Focused Therapy (EFT). EFT-CoT operationalizes intervention as a three-stage workflow: Embodied Perception, Cognitive Exploration, and Narrative Intervention. The framework employs eight specialized agents to model key processes including somatic awareness mapping, adaptive evaluation, core belief extraction, and narrative restructuring. Based on this framework, we construct EFT-Instruct, a high-quality instruction-tuning dataset built from process-level augmentation of about 67,000 real help-seeking texts, and further fine-tune a dedicated model, EFT-LLM. Experiments show that EFT-LLM consistently outperforms strong baselines and human responses in empathic depth and structural professionalism. Ablation studies further verify the contribution of key mechanisms, while white-box auditing demonstrates the consistency and traceability of critical intermediate states. Overall, this work provides a reproducible framework-data-model pipeline for embedding EFT mechanisms into LLM-based mental health support.
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。
