TableNet is a new large-scale table dataset created via LLM multi-agent generation, combined with diversity-based active learning that achieves competitive performance on its test set and superior results on real-world tables using fewer samples than baselines.
InProceedings of the IEEE conference on computer vision and pattern recognition, 9368–9377
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.
citing papers explorer
-
TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous
TableNet is a new large-scale table dataset created via LLM multi-agent generation, combined with diversity-based active learning that achieves competitive performance on its test set and superior results on real-world tables using fewer samples than baselines.
-
CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning
CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.