TOOL-ED：借助大语言模型的工具调用能力增强共情回复生成
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM
AI总结：
针对大语言模型共情回复生成依赖固定知识库、易引入噪声的问题，提出EKTC框架将常识知识库封装为共情工具以灵活调用外部知识，构建TOOL-ED数据集适配任务，实验验证可有效提升共情回复生成能力。
摘要：
共情对话是人际日常交流中的一项关键特征。当前，大语言模型（LLM）已在生成共情回复方面展现出优异性能。COMET等知识库可辅助LLM减轻幻觉问题，提升对用户意图与情绪的理解能力。但现有模型仍高度依赖固定知识库，且无限制地引入外部知识会带来噪声。工具学习是一种灵活的端到端方法，可辅助LLM处理复杂问题。本文提出情绪知识工具调用（Emotional Knowledge Tool Calling，EKTC）框架，将常识知识库封装为共情工具，使LLM能通过工具调用灵活整合外部知识。为让模型适配新任务，我们基于EMPATHETIC DIALOGUE（ED）数据集构建了新数据集TOOL-ED。我们在ED数据集上对EKTC进行了验证，实验结果表明该框架能有效提升LLM生成共情回复的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
