New Framework Combines LLMs and Graph Neural Networks to Improve Learning with Limited Labeled Data

Researchers introduced GNN-as-Judge, a framework that leverages both Large Language Models and Graph Neural Networks to improve performance on text-attributed graphs with scarce labeled data. The method uses collaborative pseudo-labeling and noise-mitigation techniques, significantly outperforming existing approaches in low-resource settings where training data is limited.