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arXiv · 2508.12854

E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model

Ronghao Lin·Shuai Shen·Weipeng Hu·Qiaolin He·Aolin Xiong·Li Huang·Haifeng Hu·Yap-peng Tan
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Abstract

Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved text-based ERG, challenges remain in handling multimodal emotional content and maintaining identity consistency. Thus, we propose E3RG, an Explicit Emotion-driven Empathetic Response Generation System based on multimodal LLMs which decomposes MERG task into three parts: multimodal empathy understanding, empathy memory retrieval, and multimodal response generation. By integrating advanced expressive speech and video generative models, E3RG delivers natural, emotionally rich, and identity-consistent responses without extra training. Experiments validate the superiority of our system on both zero-shot and few-shot settings, securing Top-1 position in the Avatar-based Multimodal Empathy Challenge on ACM MM 25. Our code is available at https://github.com/RH-Lin/E3RG.

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Ronghao Lin, Shuai Shen, Weipeng Hu, Qiaolin He, Aolin Xiong, Li Huang, Haifeng Hu, Yap-peng Tan. 2025-08-18. E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model. https://arxiv.org/abs/2508.12854

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