将共情生成拆解为分析与合成流程，提升对话模型的深度与自然度。
Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models
- 把共情任务分解为分析与生成两阶段，模拟人类认知过程。
- 在自动评估和大模型评分中均显著优于基线方法。
- 适合希望提升对话系统共情能力的研究者与开发者。
共情响应生成是构建更人性化、支持性对话代理的关键任务。然而，现有方法在专业化模型的分析深度与大语言模型（LLMs）的生成流畅性之间面临核心权衡。为此，我们提出TRACE（Task-decomposed Reasoning for Affective Communication and Empathy），一种将共情建模为结构化认知过程的新框架，通过将任务分解为分析与合成的流水线实现。在生成前建立全面理解，使深层分析与丰富表达得以统一。实验结果表明，该框架在自动评估与基于LLM的评价中均显著优于强基线，证实结构化分解是构建更强大且可解释共情代理的有前景范式。代码已公开于 https://anonymous.4open.science/r/TRACE-18EF/README.md。
原文摘要 · Abstract (English)
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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