用知识图谱提升大模型对话质量，解决幻觉与情感不一致问题
Information for Conversation Generation: Proposals Utilising Knowledge Graphs
- 动态知识图嵌入与推荐，实时引入相关外部信息
- 添加情感值实体特征，使回复更符合用户情绪
- 通过叙事气泡结构保持角色一致性，适合角色对话场景
大语言模型常用于对话生成，但缺乏外部信息时易产生低质量回复、幻觉，且表现情感能力弱、角色不连贯。知识图谱作为外部知识载体，可缓解这些问题。本文提出三项基于知识图谱的改进方案：首先，采用动态知识图嵌入与推荐机制，实现新信息的实时整合与相关知识的精准选择；其次，将带有情感值的实体作为额外特征存储，使生成回复更契合用户输入的情感基调；最后，通过叙事气泡结构整合角色信息，保障对话角色的一致性，并支持新信息的便捷接入。
原文摘要 · Abstract (English)
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.
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