TABALIGN pairs a diffusion language model planner emitting binary cell masks with a trained attention verifier, raising average accuracy 15.76 points over strong baselines on eight table benchmarks while speeding execution 44.64%.
TaBERT: Pretraining for joint understanding of textual and tabular data
3 Pith papers cite this work. Polarity classification is still indexing.
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TEmBed benchmark shows that the best tabular embedding model depends on the specific task and the representation level (cell, row, column, or table).
A BART-GraphSAGE hybrid achieves ROC-AUC 67.40 on one RelBench task, competitive with LightGBM but still behind specialized relational deep learning and foundation models.
citing papers explorer
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From Table to Cell: Attention for Better Reasoning with TABALIGN
TABALIGN pairs a diffusion language model planner emitting binary cell masks with a trained attention verifier, raising average accuracy 15.76 points over strong baselines on eight table benchmarks while speeding execution 44.64%.
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Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
TEmBed benchmark shows that the best tabular embedding model depends on the specific task and the representation level (cell, row, column, or table).
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Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
A BART-GraphSAGE hybrid achieves ROC-AUC 67.40 on one RelBench task, competitive with LightGBM but still behind specialized relational deep learning and foundation models.