  Having the ability to empathize is crucial for accurately representing human
behavior during conversations. Despite numerous research aim to improve the
cognitive capability of models by incorporating external knowledge, there has
been limited attention on the sensible and rational expression of the
conversation itself, which are crucial components of the cognitive empathy.
Guided by self-presentation theory in sociology, we have designed an innovative
categorical approach that segregates historical dialogues into sensible and
rational sentences and subsequently elucidate the context through the designed
attention mechanism. However, the rational information within the conversation
is restricted and the external knowledge used in previous methods have
limitations of semantic contradiction and narrow vision field. Considering the
impressive performance of LLM in the domain of intelligent agent. We employ
LLaMA2-70b as a rational brain to analyze the profound logical information
maintained in conversations, which assists the model assessing the balance of
sensibility and rationality to produce quality empathetic responses.
Experimental evaluations demonstrate that our method outperforms other
comparable methods on both automatic and human evaluations.
