Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
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 more effectively and efficiently. In this paper, we challenge this belief: \textit{Do GTokenLLMs fully understand graph tokens in the natural-language embedding space?} Motivated by this question, we formalize a unified framework for GTokenLLMs and propose an evaluation pipeline, \textbf{GTEval}, to assess graph-token understandin...
| Authors | Zhongjian Zhang, Yue Yu, Mengmei Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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