Position: How can Graphs Help Large Language Models?

📰 ArXiv cs.AI

Learn how graphs can enhance large language models by providing up-to-date knowledge, improving text encoding, and enabling better reasoning

advanced Published 5 May 2026

Action Steps

Build a knowledge graph to integrate with an LLM
Use graph-based methods to encode textual features
Apply graph reasoning techniques to improve LLM performance
Configure an LLM to leverage graph-structured data
Test the effectiveness of graph-enhanced LLMs on benchmark tasks

Who Needs to Know This

NLP engineers and researchers can benefit from understanding how graphs can improve LLMs, leading to better performance and more accurate results

Key Insight

💡 Graphs can provide an up-to-date knowledge source, improve text encoding, and enable better reasoning for LLMs

Key Takeaways

Learn how graphs can enhance large language models by providing up-to-date knowledge, improving text encoding, and enabling better reasoning

Full Article

Title: Position: How can Graphs Help Large Language Models?

Abstract:
arXiv:2605.02452v1 Announce Type: new 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 r

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