  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.
