Utility of Knowledge Graphs in Enhancing Cloud-Based LLMs: A Meta-Analysis

With the growing complexity of tasks requiring factual accuracy, multi-hop reasoning, and domain-specific knowledge, knowledge graphs (KGs) have emerged as a valuable complement to large language models (LLMs). However, as tasks and experimental setups vary due to different objectives, some studies report improved question answering performance through combination, while others report only negligible effects. Therefore, in this article, we review and synthesize findings from a range of studies that explore various methods for integrating KGs with LLMs, focusing on their impact on model performance. Our analysis highlights the strengths and limitations of current integration strategies, identifies emerging trends in KG-LLM research, and suggests areas for further investigation. This paper also examines how KGs contribute to overcoming the inherent challenges faced by LLMs, such as hallucination and inefficiency, and explores the potential for further advancements in combining structured knowledge with LLMs to achieve more reliable, scalable, and interpretable systems.