GTEval: Benchmarking Graph-Token Understanding in LLMs
A new evaluation pipeline called GTEval systematically assesses whether Graph-Tokenizing Large Language Models (GTokenLLMs) truly understand graph tokens in the natural-language embedding space. The study, published on arXiv (2605.03514), tests six representative GTokenLLMs using instruction transformations at format and content levels. Initial findings reveal that existing models do not fully comprehend graph tokens, challenging the prevailing assumption that LLMs can effectively process graph data through tokenization.
Key facts
- GTEval is a new evaluation pipeline for GTokenLLMs.
- The study tests 6 representative GTokenLLMs.
- Instruction transformations are applied at format and content levels.
- Existing GTokenLLMs do not fully understand graph tokens.
- The paper is published on arXiv with ID 2605.03514.
- The research challenges the belief that LLMs understand graphs effectively via tokenization.
Entities
Institutions
- arXiv