EFT-CoT: A Multi-Agent Chain-of-Thought Framework for Emotion-Focused Therapy

TL;DR

This paper introduces EFT-CoT, a multi-agent framework grounded in Emotion-Focused Therapy, enhancing mental health question answering by modeling embodied experience and primary emotion processing with specialized agents and a new fine-tuned LLM.

Contribution

It presents a novel multi-agent chain-of-thought framework for EFT in mental health support, along with a large instruction-tuning dataset and a fine-tuned model outperforming baselines.

Findings

EFT-LLM outperforms baselines and human responses in empathy and professionalism.

The framework effectively models key EFT processes through specialized agents.

A new dataset of 67,000 help-seeking texts enhances model training.

Abstract

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.