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TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering

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arxiv 2310.15075 v1 pith:7SKEYER6 submitted 2023-10-23 cs.CL

Fangyu Lei , Tongxu Luo , Pengqi Yang , Weihao Liu , Hanwen Liu , Jiahe Lei , Yiming Huang , Yifan Wei
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Shizhu He Jun Zhao Kang Liu
This is my paper
classification cs.CL
keywords tableqatableqakittoolkitcomprehensivedatasetsansweringincludesinterface
verification ladder T0 review T1 audit T2 compute T3 formal
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Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions. This paper introduces TableQAKit, the first comprehensive toolkit designed specifically for TableQA. The toolkit designs a unified platform that includes plentiful TableQA datasets and integrates popular methods of this task as well as large language models (LLMs). Users can add their datasets and methods according to the friendly interface. Also, pleasantly surprised using the modules in this toolkit achieves new SOTA on some datasets. Finally, \tableqakit{} also provides an LLM-based TableQA Benchmark for evaluating the role of LLMs in TableQA. TableQAKit is open-source with an interactive interface that includes visual operations, and comprehensive data for ease of use.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

    cs.CL 2025-06 conditional novelty 5.0 of 10

    TReB evaluates 26 large language models on 26 table reasoning subtasks using textual, programmatic, and interleaved reasoning modes, finding that the best model reaches only about 70 on a 0-100 judging scale.

  2. Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges

    cs.CL 2025-07 conditional novelty 3.0 of 10

    A structured review of table understanding with LLMs that proposes a taxonomy of input representations and identifies three research gaps.

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