Position: How can Graphs Help Large Language Models?

2026-05-04

AI Summary

This work explores how graph structures can enhance large language models (LLMs) to mitigate hallucinations, strengthen complex reasoning, and improve comprehension of structured data. To this end, the authors propose a systematic graph-augmented LLM paradigm that reduces factual errors through dynamic knowledge injection, introduces graph-based prompting strategies—such as Graph-of-Thought—to reinforce reasoning chains, and integrates graph neural networks with knowledge graphs to model structured information from domains like e-commerce, source code, and relational databases. Experimental results demonstrate that the proposed approach significantly lowers hallucination rates and achieves notable performance gains across diverse structured reasoning tasks, while also opening new avenues for graph-based sparse architectures and brain-inspired memory systems.

Abstract

With the rapid advancement of large language models (LLMs), classic graph learning tasks have greatly benefited from LLMs, including improved encoding of textual features, more efficient construction of graphs from text, and enhanced reasoning over knowledge graphs. In this paper, we ask a complementary question: How can graphs help LLMs? We address this question from three perspectives: 1) graphs provide an up-to-date knowledge source that helps reduce LLM hallucinations, 2) graph-based prompting techniques-such as Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT)-enhance LLM reasoning capabilities, and 3) integrating graphs into LLMs improves their understanding of structured data, expanding their applicability to domains such as e-commerce, code, and relational databases (RDBs). We further outlook some future directions including designing sparse LLM architectures based on graphs and brain-inspired memory systems.

Problem

• Graphs
• Large Language Models
• Hallucination
• Reasoning
• Structured Data

Innovation

• graph-augmented LLMs
• hallucination reduction
• graph-based prompting
• structured data understanding
• Graph-of-Thought