A Comprehensive Review of Knowledge Graph Integration in Large Language Models for Trust: Challenges and Future Directions

Large Language Models (LLMs) have transformed the landscape of natural language processing, enabling significant application advancements. However, LLMs’ inherent complexity and opacity raise critical concerns regarding their trustworthiness, including bias, misinformation, and lack of interpretability. This paper presents a comprehensive state-of-the-art review on the integration of knowledge graphs (KGs) to enhance trust in LLMs. We explore how KGs can serve as structured frameworks for contextualizing the knowledge embedded in LLMs, providing a means to verify and validate their outputs against reliable sources of information. The review highlights various methodologies that leverage KGs to address trust-related challenges in LLMs, including mechanisms for improving factual consistency, reducing biases, and enhancing interpretability. Additionally, we analyze recent advancements and empirical studies that demonstrate the efficacy of knowledge graph integration in fostering transparency and reliability in LLM applications. By synthesizing the current landscape of research, this paper aims to identify future directions and key challenges in developing trust-aware systems that utilize the synergistic potential of LLMs and knowledge graphs.