A Graph Perspective to Probe Structural Patterns of Knowledge in Large
  Language Models
Large language models have been extensively studied as neural knowledge bases
for their knowledge access, editability, reasoning, and explainability.
However, few works focus on the structural patterns of their knowledge.
Motivated by this gap, we investigate these structural patterns from a graph
perspective. We quantify the knowledge of LLMs at both the triplet and entity
levels, and analyze how it relates to graph structural properties such as node
degree. Furthermore, we uncover the knowledge homophily, where topologically
close entities exhibit similar levels of knowledgeability, which further
motivates us to develop graph machine learning models to estimate entity
knowledge based on its local neighbors. This model further enables valuable
knowledge checking by selecting triplets less known to LLMs. Empirical results
show that using selected triplets for fine-tuning leads to superior
performance.
6839220cf85de1fc56402b3d
Utkarsh Sahu
6839220cf85de1fc56402b3e
Zhisheng Qi
6839220cf85de1fc56402b3f
Yongjia Lei
6839220cf85de1fc56402b40
Ryan A. Rossi
6839220cf85de1fc56402b41
Franck Dernoncourt
claimed_verified
2025-05-30T06:53:31.804Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
6839220cf85de1fc56402b42
Nesreen K. Ahmed
6839220cf85de1fc56402b43
Mahantesh M Halappanavar
extracted_confirmed
2025-05-30T05:25:34.615Z
637c6d95a8716d642050b50f
/avatars/0955a10113807348f24db968c7bd7c7a.svg
Mahantesh Halappanavar
user
mhalappa
6839220cf85de1fc56402b44
Yao Ma
6839220cf85de1fc56402b45
Yu Wang
A Graph Perspective to Probe Structural Patterns of Knowledge in Large
  Language Models
Large language models have been extensively studied as neural knowledge bases
for their knowledge access, editability, reasoning, and explainability.
However, few works focus on the structural patterns of their knowledge.
Motivated by this gap, we investigate these structural patterns from a graph
perspective. We quantify the knowledge of LLMs at both the triplet and entity
levels, and analyze how it relates to graph structural properties such as node
degree. Furthermore, we uncover the knowledge homophily, where topologically
close entities exhibit similar levels of knowledgeability, which further
motivates us to develop graph machine learning models to estimate entity
knowledge based on its local neighbors. This model further enables valuable
knowledge checking by selecting triplets less known to LLMs. Empirical results
show that using selected triplets for fine-tuning leads to superior
performance.
The study explores the structural patterns of knowledge in large language models from a graph perspective, uncovering knowledge homophily and developing models for graph machine learning to estimate entity knowledge.
large language models graph perspective triplet entity node degree knowledge homophily graph machine learning knowledge checking fine-tuning