Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of developing a graph foundation model that generalizes diverse scenarios. The key challenge is to align graph data with language spaces so that LLMs can better comprehend graphs. As a popular paradigm, Graph-Tokenizing LLMs (GTokenLLMs) encode complex structures and lengthy texts into a graph token sequence, and then align them with text tokens via language instructions tuning. Despite their initial success, our information-theoretic analysis reveals that existing GTokenLLMs rely solely on text supervision from lang...
| Authors | Zhongjian Zhang, Xiao Wang, Mengmei Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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