Emotion-Aware LLM Adaptation for Empathetic Dialogue Generation

The increasing emphasis on spiritual well-being in contemporary society has fueled a growing demand for empathetic dialogue generation. While large language models (LLMs) have demonstrated remarkable performance in dialogue generation tasks, their empathetic capabilities remain limited due to the absence of explicit emotional guidance. To address this limitation, we propose EmoDiag, an emotion-aware LLM adaptation framework designed to enhance the empathetic abilities of LLMs through fine-grained emotional control and a non-intrusive adaptation mechanism using the residual memory transformer (RMT). Specifically, EmoDiag employs RMT's encoder to predict the target emotional state from past conversations, which serves as an explicit control condition. Then, EmoDiag seamlessly integrates the predicted emotional state into the LLM's generation process through RMT's decoder at the logit level, enabling emotion-aware dialogue generation in a plug-and-play manner. Experimental results demonstrate that EmoDiag significantly outperforms baseline models in empathetic dialogue generation tasks and enhances the empathetic capacity of LLMs, while maintaining high-quality text generation in terms of diversity, fluency, and relevance.