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arXiv · 2509.21205

TABLET: A Large-Scale Dataset for Robust Visual Table Understanding

Iñigo Alonso·Imanol Miranda·Eneko Agirre·Mirella Lapata
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Abstract

While table understanding increasingly relies on pixel-only settings, current benchmarks predominantly use synthetic renderings that lack the complexity and visual diversity of real-world tables. Additionally, existing visual table understanding (VTU) datasets offer fixed examples with single visualizations and pre-defined instructions, providing no access to underlying serialized data for reformulation. We introduce TABLET, a large-scale VTU dataset with 4 million examples across 21 tasks, grounded in 2 million unique tables where 88% preserve original visualizations. To evaluate whether models are able to jointly reason over tabular and visual content, we also introduce VisualTableQA, a benchmark requiring both visual perception and table understanding. Fine-tuning vision-language models like Qwen2.5-VL-7B and Gemma 3-4B on TABLET improves performance on seen and unseen VTU tasks while increasing robustness on real-world table visualizations. By preserving original visualizations and maintaining example traceability in a unified large-scale collection, TABLET establishes a foundation for robust training and extensible evaluation of future VTU models.

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Iñigo Alonso, Imanol Miranda, Eneko Agirre, Mirella Lapata. 2025-09-25. TABLET: A Large-Scale Dataset for Robust Visual Table Understanding. https://arxiv.org/abs/2509.21205

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