APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation

TL;DR

This paper introduces APTNESS, a framework that enhances large language models' empathetic response generation by integrating appraisal theory, emotional support strategies, and retrieval mechanisms to improve both cognitive and affective empathy.

Contribution

It presents a novel framework combining retrieval augmentation and emotional support strategies based on appraisal theory to improve empathetic responses in language models.

Findings

Enhanced empathy in LLMs from cognitive and affective perspectives.

Improved response quality using a comprehensive emotional palette.

Framework outperforms baselines in empathetic response tasks.

Abstract

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.