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 directly with hidden representations. Instantiating this paradigm following low-rank adaptation (LoRA), we introduce GaRA, a Graph-aware LoRA generation model. GaRA constructs low-rank weight updates conditioned on the original graph structures and constrains the norm of the generated updates, thus injecting whole-graph information and avoiding the optimization bias in the weight generation. Empirical studies demonstrate that GaRA consistently outperforms baselines on zero-shot graph learning tasks.

We proposed a novel weight-level injection paradigm for adapting LMs to graph tasks. Unlike prior input- or output-level methods, which either overlook higher-level information from the whole-graph context or struggle to generalize across tasks, our paradigm enables explicit whole-graph injection by generating weight updates during fine-tuning and applying them to hidden-layer representations. In this way, weight-level injection complements subgraph information introduced at the input level and mitigates potential information loss.