Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909993879470080 |
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| author | Liu, Ziqi Zhou, Ziyang Li, Yilin Zhang, Haiyang Chen, Yangbin |
| author_facet | Liu, Ziqi Zhou, Ziyang Li, Yilin Zhang, Haiyang Chen, Yangbin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models Liu, Ziqi Zhou, Ziyang Li, Yilin Zhang, Haiyang Chen, Yangbin Computation and Language Multiagent Systems 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. |
| title | Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models |
| topic | Computation and Language Multiagent Systems |
| url | https://arxiv.org/abs/2509.21849 |