让大模型用工具调用共情知识，更精准回应用户情绪。
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM
- 将共情知识库封装为可调用工具，灵活获取外部信息。
- 在ED数据集上，模型生成回应的共情度提升12.3%。
- 适合需要高情感理解的对话系统研发者使用。
共情对话是人际交流中的关键特征。当前大语言模型（LLMs）在生成共情回应方面表现优异，知识库如COMET可帮助模型减少幻觉，增强对用户意图与情绪的理解。然而，模型仍过度依赖固定知识库，且无限制引入外部知识可能带来噪声。工具学习是一种灵活的端到端方法，有助于大模型处理复杂任务。本文提出情感知识工具调用（EKTC）框架，将常识知识库封装为共情工具，使大模型可通过工具调用灵活整合外部知识。为适应新任务，我们基于EMPATHETIC DIALOGUE（ED）数据集构建了新数据集TOOL-ED。在ED数据集上的实验表明，该框架能有效提升大模型生成共情回应的能力。
原文摘要 · Abstract (English)
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.
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