Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
Graph neural networks (GNNs) tightly couple their input-output parameters to dataset-specific feature spaces and target sets, exhibiting limited transferability across different datasets. In contrast, language models (LMs) generalize flexibly via a unified input-output interface, motivating recent attempts to adapt LMs to graph tasks. However, existing methods struggle to encode whole-graph information, leading to potential information loss and suboptimal graph understanding. In this work, we propose a novel weight-level information injection paradigm for adapting LMs to graph tasks. This paradigm injects whole-graph information by generating task-specific weight updates that interact dir...
| Authors | Junshu Sun, Wanxing Chang, Qingming Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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