  Empathetic response generation is designed to comprehend the emotions of
others and select the most appropriate strategies to assist them in resolving
emotional challenges. Empathy can be categorized into cognitive empathy and
affective empathy. The former pertains to the ability to understand and discern
the emotional issues and situations of others, while the latter involves the
capacity to provide comfort. To enhance one's empathetic abilities, it is
essential to develop both these aspects. Therefore, we develop an innovative
framework that combines retrieval augmentation and emotional support strategy
integration. Our framework starts with the introduction of a comprehensive
emotional palette for empathy. We then apply appraisal theory to decompose this
palette and create a database of empathetic responses. This database serves as
an external resource and enhances the LLM's empathy by integrating semantic
retrieval mechanisms. Moreover, our framework places a strong emphasis on the
proper articulation of response strategies. By incorporating emotional support
strategies, we aim to enrich the model's capabilities in both cognitive and
affective empathy, leading to a more nuanced and comprehensive empathetic
response. Finally, we extract datasets ED and ET from the empathetic dialogue
dataset \textsc{EmpatheticDialogues} and ExTES based on dialogue length.
Experiments demonstrate that our framework can enhance the empathy ability of
LLMs from both cognitive and affective empathy perspectives. Our code is
released at https://github.com/CAS-SIAT-XinHai/APTNESS.
