The contemporary LLMs are prone to producing hallucinations, stemming mainly from the knowledge gaps within the models. To address this critical limitation, researchers employ diverse strategies to augment the LLMs by incorporating external knowledge, aiming to reduce hallucinations and enhance reasoning accuracy. Among these strategies, leveraging knowledge graphs as a source of external information has demonstrated promising results. In this survey, we conduct a comprehensive review of these knowledge-graph-based knowledge augmentation techniques in LLMs, focusing on their efficacy in mitigating hallucinations. We systematically categorize these methods into three overarching groups, offering both methodological comparisons and empirical evaluations of their performance. Lastly, the paper explores the challenges associated with these techniques and outlines potential avenues for future research in this emerging field.

翻译：当代大语言模型容易产生幻觉，这主要源于模型内部的知识缺口。为解决这一关键缺陷，研究人员采用多种策略通过引入外部知识增强大语言模型，旨在减少幻觉并提升推理准确性。其中，利用知识图谱作为外部信息来源的策略已展现出显著成效。本综述全面回顾了大语言模型中基于知识图谱的知识增强技术，重点探讨其在缓解幻觉方面的有效性。我们系统性地将这些方法分为三大类别，进行方法论比较与性能实证评估。最后，本文探讨了这些技术面临的挑战，并概述了这一新兴领域未来的潜在研究方向。