TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM

TL;DR

This paper introduces the EKTC framework that enables LLMs to call external empathetic knowledge tools, improving their ability to generate more empathetic responses in conversations.

Contribution

The paper proposes a novel tool calling framework for LLMs that incorporates external knowledge bases to enhance empathetic response generation.

Findings

EKTC improves empathetic response quality in LLMs.

The framework effectively integrates external knowledge without noise.

Experimental results show significant performance gains.

Abstract

Empathetic conversation is a crucial characteristic in daily conversations between individuals. Nowadays, Large Language models (LLMs) have shown outstanding performance in generating empathetic responses. Knowledge bases like COMET can assist LLMs in mitigating illusions and enhancing the understanding of users' intentions and emotions. However, models remain heavily reliant on fixed knowledge bases and unrestricted incorporation of external knowledge can introduce noise. Tool learning is a flexible end-to-end approach that assists LLMs in handling complex problems. In this paper, we propose Emotional Knowledge Tool Calling (EKTC) framework, which encapsulates the commonsense knowledge bases as empathetic tools, enabling LLMs to integrate external knowledge flexibly through tool calling. In order to adapt the models to the new task, we construct a novel dataset TOOL-ED based on the EMPATHETICMPATHETIC DIALOGUE (ED) dataset. We validate EKTC on the ED dataset, and the experimental results demonstrate that our framework can enhance the ability of LLMs to generate empathetic responses effectively.