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GTAlign achieves superior performance in graph classification tasks without the need for textual data, challenging the reliance on traditional graph neural networks and LLMs.
Tabular data synthesis no longer needs to sacrifice privacy for quality: pretraining on diverse datasets lets models generalize from limited context, breaking the traditional tradeoff.
