AI RESEARCH
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
arXiv CS.LG
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ArXi:2605.03514v1 Announce Type: cross The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a widely recognized paradigm, Graph-Tokenizing LLMs (GTokenLLMs) compress complex graph data into graph tokens and treat them as prefix tokens for querying LLMs, leading many to believe that LLMs can understand graphs effectively and efficiently.