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arXiv · 2509.21849

Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models

Ziqi Liu·Ziyang Zhou·Yilin Li·Haiyang Zhang·Yangbin Chen
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Abstract

Empathetic response generation is a crucial task for creating more human-like and supportive conversational agents. However, existing methods face a core trade-off between the analytical depth of specialized models and the generative fluency of Large Language Models (LLMs). To address this, we propose TRACE, Task-decomposed Reasoning for Affective Communication and Empathy, a novel framework that models empathy as a structured cognitive process by decomposing the task into a pipeline for analysis and synthesis. By building a comprehensive understanding before generation, TRACE unites deep analysis with expressive generation. Experimental results show that our framework significantly outperforms strong baselines in both automatic and LLM-based evaluations, confirming that our structured decomposition is a promising paradigm for creating more capable and interpretable empathetic agents. Our code is available at https://anonymous.4open.science/r/TRACE-18EF/README.md.

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Ziqi Liu, Ziyang Zhou, Yilin Li, Haiyang Zhang, Yangbin Chen. 2025-09-26. Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models. https://arxiv.org/abs/2509.21849

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