EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning

Abstract

Empathetic response generation aims to understand the user’s feelings emotionally and generate responses with appropriate emotion. According to psychological theories, empathy consists of two main aspects: affection and cognition. However, existing works lack the perception of fine-grained dialogue emotion propagation, as well as have limitations in reasoning about the intentions of users on cognition, which affect the quality of empathetic response. To this end, we propose to generate Empathetic response based on in-context Commonsense reasoning and Reinforcement Learning (EmpCRL). First, we use a current popular large language model combined with multi-view contextual reasoning to broaden the cognitive boundaries through in-context learning. Furthermore, we infer the response emotion by jointly modeling the dialogue history and emotion flow, and achieve the control of response emotion and diversity through reinforcement learning. Extensive experiments on EmpatheticDialogues dataset show that our model outperforms state-of-the-art models in both automatic and human evaluation.