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

2025-09-26

AI Summary

Existing empathic response generation methods struggle to simultaneously achieve the analytical depth of specialized models and the generative fluency of large language models (LLMs). To address this, we propose TRACE—a structured, interpretable cognitive framework for empathy modeling, decomposing empathy into four sequential stages: Recognition → Understanding → Mapping → Expression. TRACE employs a multi-agent architecture that orchestrates domain-specific emotion analysis modules with an LLM via task decomposition, enabling tight integration of deep affective understanding and natural language generation. Compared to end-to-end baselines, TRACE achieves statistically significant improvements in both automated metrics (BLEU, BERTScore, Emotion-F1) and LLM-based human evaluation, demonstrating superior empathic quality and interpretability. These results validate the efficacy and advantages of structuring empathy as an explicit cognitive pipeline for enhancing empathic capabilities in conversational systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Balancing analytical depth with generative fluency in empathetic agents
• Decomposing empathy into structured cognitive analysis and synthesis
• Creating interpretable empathetic responses through task decomposition

Innovation

• Decomposes empathy into structured cognitive process
• Unites deep analysis with expressive generation
• Uses multi-agent pipeline for affective communication