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.

本研究解决了大语言模型在对话生成中因缺乏相关内容和存在幻觉而产生的低质量响应问题。通过动态知识图谱嵌入、情感价值实体存储和叙事泡泡整合角色信息等新颖方法，提升了生成质量和情感一致性。这项研究显示，知识图谱的应用能够显著改善对话生成的情感对齐和角色连贯性。 

对话生成的信息：利用知识图谱的提案