用于对话生成的信息：利用知识图谱的提案
Information for Conversation Generation: Proposals Utilising Knowledge Graphs
AI总结：
本文提出三项利用知识图谱增强大语言模型对话生成的方案，分别解决动态知识整合、情感对齐和角色一致性问题。
摘要：
大语言模型（LLMs）是对话生成中常用的工具。在缺乏额外信息的情况下，LLMs 可能因缺少相关内容、产生幻觉、情感能力表现不佳以及无法保持一致的角色设定而生成质量较低的回复。知识图谱是常用的外部知识形式，可能为这些挑战提供解决方案。本文提出三项利用知识图谱增强 LLM 生成的提案。第一，动态知识图谱嵌入与推荐可以整合新信息，并为回复生成选择相关知识。第二，将带有情感值的实体作为附加特征进行存储，可以提供与用户输入在情感上更对齐的知识。第三，通过叙事气泡整合角色信息，可以保持角色一致性，并引入一种能够随时纳入新信息的结构。
英文摘要：
LLMs are frequently used tools for conversational generation. Without additional information LLMs can generate lower quality responses due to lacking relevant content and hallucinations, as well as the perception of poor emotional capability, and an inability to maintain a consistent character. Knowledge graphs are commonly used forms of external knowledge and may provide solutions to these challenges. This paper introduces three proposals, utilizing knowledge graphs to enhance LLM generation. Firstly, dynamic knowledge graph embeddings and recommendation could allow for the integration of new information and the selection of relevant knowledge for response generation. Secondly, storing entities with emotional values as additional features may provide knowledge that is better emotionally aligned with the user input. Thirdly, integrating character information through narrative bubbles would maintain character consistency, as well as introducing a structure that would readily incorporate new information.
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。
