Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey
It tackles the problem of hallucinations in LLMs for users relying on accurate AI-generated content, but it is incremental as it surveys existing methods rather than introducing new ones.
This survey reviews knowledge-graph-based augmentation techniques for LLMs to address hallucinations caused by knowledge gaps, finding that these methods show promising results in reducing hallucinations and enhancing reasoning accuracy.
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 comprehensively review these knowledge-graph-based augmentation techniques in LLMs, focusing on their efficacy in mitigating hallucinations. We systematically categorize these methods into three overarching groups, offering methodological comparisons and performance evaluations. Lastly, this survey explores the current trends and challenges associated with these techniques and outlines potential avenues for future research in this emerging field.