Empathetic response generation is increasingly significant in AI, necessitating nuanced emotional and cognitive understanding coupled with articulate response expression. Current large language models (LLMs) excel in response expression; however, they lack the ability to deeply understand emotional and cognitive nuances, particularly in pinpointing fine-grained emotions and their triggers. Conversely, small-scale empathetic models (SEMs) offer strength in fine-grained emotion detection and detailed emotion cause identification. To harness the complementary strengths of both LLMs and SEMs, we introduce a Hybrid Empathetic Framework (HEF). HEF regards SEMs as flexible plugins to improve LLM's nuanced emotional and cognitive understanding. Regarding emotional understanding, HEF implements a two-stage emotion prediction strategy, encouraging LLMs to prioritize primary emotions emphasized by SEMs, followed by other categories, substantially alleviates the difficulties for LLMs in fine-grained emotion detection. Regarding cognitive understanding, HEF employs an emotion cause perception strategy, prompting LLMs to focus on crucial emotion-eliciting words identified by SEMs, thus boosting LLMs' capabilities in identifying emotion causes. This collaborative approach enables LLMs to discern emotions more precisely and formulate empathetic responses. We validate HEF on the Empathetic-Dialogue dataset, and the findings indicate that our framework enhances the refined understanding of LLMs and their ability to convey empathetic responses.

翻译：共情回复生成在人工智能领域日益重要，需要模型具备细腻的情感和认知理解能力，同时能够清晰表达回复。当前的大语言模型（LLMs）在回复表达方面表现出色，但在深入理解情感和认知细微差别方面存在不足，特别是在精准识别细粒度情感及其触发因素方面。相比之下，小规模共情模型（SEMs）在细粒度情感检测和详细情感原因识别方面具有优势。为充分发挥LLMs和SEMs的互补优势，我们提出了一种混合共情框架（HEF）。HEF将SEMs视为灵活插件，用于提升LLMs对情感和认知细微差别的理解能力。在情感理解方面，HEF实施两阶段情感预测策略，引导LLMs优先关注SEMs强调的主要情感，再处理其他类别，显著减轻了LLMs在细粒度情感检测中的难度。在认知理解方面，HEF采用情感原因感知策略，促使LLMs聚焦于SEMs识别出的关键情感诱发词，从而增强LLMs识别情感原因的能力。这种协作方法使LLMs能够更精确地辨别情感，并生成共情回复。我们在Empathetic-Dialogue数据集上验证了HEF，结果表明我们的框架提升了LLMs的精细理解能力及其传达共情回复的能力。