Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models
- URL: http://arxiv.org/abs/2509.21849v1
- Date: Fri, 26 Sep 2025 04:20:37 GMT
- Title: Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models
- Authors: Ziqi Liu, Ziyang Zhou, Yilin Li, Haiyang Zhang, Yangbin Chen,
- Abstract summary: Empathetic response generation is a crucial task for creating more human-like and supportive conversational agents.<n>Existing methods face a core trade-off between the analytical depth of specialized models and the generative fluency of Large Language Models.<n>We propose TRACE, a novel framework that models empathy as a structured cognitive process by decomposing the task into a pipeline for analysis and synthesis.
- Score: 19.450298798183166
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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