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New study finds graph-tokenizing LLMs lack full graph understanding

A new paper systematically evaluates graph-tokenizing large language models (GTokenLLMs) and finds they do not fully understand graph tokens. The research introduces GTEval, an evaluation pipeline designed to assess graph-token understanding through instruction transformations. Experiments reveal that current GTokenLLMs are overly reliant on text for reasoning and their utilization of graph tokens varies significantly across models and instructions, even with additional tuning. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Highlights limitations in current LLM capabilities for graph understanding, suggesting a need for improved methods beyond simple tokenization.

RANK_REASON The cluster contains an academic paper evaluating existing models and proposing a new evaluation framework.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Zhongjian Zhang, Yue Yu, Mengmei Zhang, Junping Du, Xiao Wang, Chuan Shi ·

    Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding

    arXiv:2605.03514v1 Announce Type: cross Abstract: 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 g…

  2. arXiv cs.CL TIER_1 · Chuan Shi ·

    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 pref…