This May 2025 paper explores the structural patterns of knowledge within Large Language Models (LLMs) by adopting a graph-based perspective. The authors quantify LLM knowledge at both the triplet and entity levels, analyzing its relationship with graph properties like node degree. Key findings include the discovery of knowledge homophily, where closely connected entities exhibit similar knowledgeability, and a positive correlation between an entity's degree and its knowledge. These insights further motivate the development of graph machine learning models to predict entity knowledge, which can then be used to strategically select less-known triplets for fine-tuning LLMs, leading to improved performance. The study evaluates several prominent LLMs across diverse knowledge graphs, highlighting domain-specific variations in knowledge distribution and the consistent presence of structural patterns.
Source:
https://arxiv.org/html/2505.19286v2
