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.

翻译：大型语言模型是对话生成的常用工具。若无额外信息支持，由于缺乏相关内容与产生幻觉，以及情感能力感知不足与角色一致性难以维持，LLM可能生成质量较低的回复。知识图谱作为常用的外部知识形式，可为解决这些挑战提供方案。本文提出三项利用知识图谱增强LLM生成的方案：首先，动态知识图谱嵌入与推荐机制可实现新信息的整合及相关知识的筛选以支持回复生成；其次，存储带有情感值的实体作为附加特征，可提供与用户输入情感更契合的知识；再次，通过叙事气泡整合角色信息，既能保持角色一致性，又能构建易于融入新信息的结构。