LLMs struggle with graph structure, text alone suffices

A new study published on arXiv challenges the conventional wisdom that explicit graph structure is always beneficial for large language models (LLMs). Researchers found that LLMs perform surprisingly well on text-attributed graphs using only node textual descriptions, with most structural encoding strategies offering minimal or even negative gains. This suggests that in the era of powerful LLMs, traditional graph learning paradigms may need to be re-evaluated, potentially favoring semantics-driven approaches over structure-centric ones. AI

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IMPACT Challenges the necessity of explicit graph structure for LLMs, potentially shifting focus to semantics-driven graph learning approaches.

RANK_REASON Academic paper presenting novel findings on LLM capabilities with graph data.

Read on arXiv cs.LG →

• arXiv
• LLMs
• Haotian Xu
• graph neural networks
• GNNs

• paper
• other