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.

翻译：共情对话是人际日常交流中的一个关键特征。如今，大语言模型在生成共情回复方面已展现出卓越的性能。诸如COMET之类的知识库可以帮助大语言模型缓解幻觉，并增强对用户意图和情绪的理解。然而，模型仍然严重依赖固定的知识库，且无限制地引入外部知识可能会带来噪声。工具学习是一种灵活的端到端方法，可协助大语言模型处理复杂问题。在本文中，我们提出了情感知识工具调用框架，该框架将常识知识库封装为共情工具，使大语言模型能够通过工具调用灵活地整合外部知识。为了使模型适应新任务，我们基于EMPATHETIC DIALOGUE数据集构建了一个新颖的数据集TOOL-ED。我们在ED数据集上验证了EKTC，实验结果表明，我们的框架能有效增强大语言模型生成共情回复的能力。