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2026-01-26 22:35:48
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TempTabQA Paper Dataset Explore Code Video Media PPT Temporal Question Answering for Semi-Structured Tables About In semi-structured data, such as Infobox tables, temporal information about entities is common but often challenging for current NLP systems to handle effectively. Our introduction of TempTabQA addresses this by presenting a rich dataset consisting of 11,454 question-answer pairs sourced from 1,208 diverse Wikipedia Infobox tables spanning over 90 distinct domains. The evaluation of leading models in this task reveals a significant gap, with even the best-performing LLMs trailing human performance by more than 13.5 F1 points. Our dataset stands as a robust benchmark, aiming to push the boundaries and improve the temporal reasoning capabilities of NLP models in handling such intricate data structures. TL;DR: TempTabQA, featuring 11,000+ Q&A pairs sourced from varied Wikipedia Infobox tables, evaluates NLP models' understanding of temporal data. Results ...
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