April 2, 2024, 7:51 p.m. | Akash Ghosh, B Venkata Sahith, Niloy Ganguly, Pawan Goyal, Mayank Singh

cs.CL updates on arXiv.org arxiv.org

arXiv:2404.00401v1 Announce Type: new
Abstract: Question-answering (QA) on hybrid scientific tabular and textual data deals with scientific information, and relies on complex numerical reasoning. In recent years, while tabular QA has seen rapid progress, understanding their robustness on scientific information is lacking due to absence of any benchmark dataset. To investigate the robustness of the existing state-of-the-art QA models on scientific hybrid tabular data, we propose a new dataset, "SciTabQA", consisting of 822 question-answer pairs from scientific tables and their …

abstract arxiv cs.cl data dataset deals hybrid information numerical progress question reasoning robust robustness scientific study tables tabular textual type understanding

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