[2604.08553] GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback

Computer Science > Machine Learning arXiv:2604.08553 (cs) [Submitted on 11 Mar 2026] Title:GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback Authors:Ruiyao Xu, Kaize Ding View a PDF of the paper titled GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback, by Ruiyao Xu and 1 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However, their effectiveness as predictors in the low-resource setting, where labeled nodes are severely limited and scarce, remains constrained since fine-tuning LLMs usually requires sufficient labeled data, especially when the TAG shows complex structural patterns. In essence, this paper targets two key challenges: (i) the difficulty of generating and selecting reliable pseudo labels on TAGs for LLMs, and (ii) the need to mitigate potential label noise when fine-tuning LLMs with pseudo labels. To counter the challenges, we propose a new framework, GNN-as-Judge, which can unleash the power of LLMs for few-shot semi-supervised learning on TAGs by incorporating the structural inductive bias of Graph Neural Networks (GNNs). Specifically, GNN-as-Judge introduces a collaborative pseudo-labeling strategy that first identifies the most influenced unlabeled nodes from labeled nodes, then exploits both the agreement and disagreement pa...