One potential limitation of the GNN-based approach in GAIT is the challenge of capturing long-range dependencies in wide tables. As the number of columns increases, the distance between columns grows, making it harder for traditional GNN architectures to effectively model these dependencies. To address this limitation, techniques like hierarchical graph neural networks can be explored. By organizing columns into hierarchical structures based on their relationships, hierarchical GNNs can capture dependencies at different levels of granularity, enabling more effective modeling of long-range dependencies in wide tables. Additionally, incorporating attention mechanisms within the GNN architecture can help prioritize relevant information and improve the model's ability to focus on important dependencies.