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.

综述了基于知识图谱的知识增强技术在 LLMs 中减轻幻觉方面的有效性，将这些方法系统地分类为三个总体组，提供了方法比较和性能的实证评估，最后探讨了这些技术所面临的挑战，并概述了这个新兴领域的未来研究方向。