Hugging Face Trending Papers
Tables Decoded: DELTA for Structure, TARQA for Understanding
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arXiv Machine Learning
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Tables Decoded: DELTA for Structure, TARQA for Understanding
arXiv:2609.17458v1 Announce Type: cross Abstract: Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering...
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TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs
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TeleOCR is a unified framework for document parsing that tackles challenges in both decoupled and end-to-end Vision‑Language Models. It introduces deformation‑aware learning to handle geometric distortions, an adaptive sampling mechanism for complex layouts, and a content‑structure decoupled strategy to model formula grammars and table structures. The approach achieves state‑of‑the‑art results on multiple benchmarks, including top placement in the ICDAR 2026 Sci‑ImageMiner Challenge.
arXiv Computation and Language
Aug 28
TabuLM: Morphology-Aware Tabular Pre-training for Low-Resource Languages
TabuLM is a new language model pre‑trained on Kinyarwanda tabular data, extending KinyaBERT‑large with row, column, and cell‑type embeddings and a table‑structure attention bias. It introduces two pre‑training objectives—Masked Cell Recovery and Column Type Prediction—and is trained on 172 Rwandan government tables. On the TabQA‑kin benchmark, TabuLM achieves 62.0% exact match, outperforming KinyaBERT‑large and multilingual baselines by significant margins.
Hugging Face Trending Papers
Aug 27
TabuLM: Morphology-Aware Tabular Pre-training for Low-Resource Languages
We present TabuLM, the first language model pre-trained on Kinyarwanda tabular data. Kinyarwanda is a morphologically rich Bantu language spoken by over 12 million people in Rwanda, yet lacks any dedi...