The recent advancements in the field of large language models (LLMs) and graph neural networks (GNNs) have shown significant promise in addressing complex challenges across various domains. A notable trend is the integration of LLMs with GNNs to enhance the semantic understanding and performance of graph-based tasks. This approach leverages the strengths of both models, with LLMs providing rich textual context and GNNs handling structural data. Specifically, the use of LLMs for data augmentation in imbalanced datasets and as ensemblers for multiple GNNs has demonstrated improved accuracy and robustness in tasks such as node classification and graph-based learning. Additionally, innovative methods like cluster-refined negative sampling in graph contrastive learning are addressing biases and improving the efficiency of text classification. These developments not only advance the capabilities of existing models but also open new avenues for research in integrating textual and structural information effectively.

Noteworthy papers include one that proposes a novel GFM solely based on LLMs, achieving state-of-the-art performance across a comprehensive benchmark, and another that introduces a lightweight pipeline for controlled text generation in LLMs, significantly enhancing accuracy and reducing aspect correlations.