用知识图谱减少大模型幻觉，提升生成可靠性。
Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective
- 将知识图谱作为外部事实源，补足大模型认知缺口。
- 现有方法在跨域知识融合与评估上仍有明显不足。
- 适合关注大模型可信生成的研究者和应用开发者。
大语言模型（LLMs）已革新自然语言处理应用，但在文本生成、问答和聊天机器人中存在幻觉问题——即生成看似合理却事实错误的内容，严重影响信任度与实际应用。知识图谱（KGs）以结构化方式存储实体及其关系，可为模型提供准确的背景知识，缓解幻觉问题，提升准确性与可靠性。尽管如此，该方向仍处于活跃研究阶段，存在诸多未解挑战。本文综述了当前主流数据集、评测基准及知识融合方法，并讨论了知识图谱在大模型系统中的应用现状，识别各环节的关键未来方向。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others. However, they face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This undermines trust and limits the applicability of LLMs in different domains. Knowledge Graphs (KGs), on the other hand, provide a structured collection of interconnected facts represented as entities (nodes) and their relationships (edges). In recent research, KGs have been leveraged to provide context that can fill gaps in an LLM understanding of certain topics offering a promising approach to mitigate hallucinations in LLMs, enhancing their reliability and accuracy while benefiting from their wide applicability. Nonetheless, it is still a very active area of research with various unresolved open problems. In this paper, we discuss these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations. In our discussion, we consider the current use of KGs in LLM systems and identify future directions within each of these challenges.
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