mlsanityData to 7 Oct 2026

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

arXiv 2310.15075 ↗First posted 23 Oct 2023cs.CLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 19 of its 45 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

19 of 45 references.

References · 19

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
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  7. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
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7 more
  1. 2021MultiModalQA: Complex Question Answering over Text, Tables and Imagescited by 177
  2. 2021A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutionscited by 77
  3. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
  4. 2022Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generationcited by 28
  5. 2023MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Imagescited by 18
  6. 2023Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Questioncited by 10
  7. 2023HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answeringcited by 5

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 2.

7 papers.

  1. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  2. 2024Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolutioncited by 19
  3. 2025Reasoning-Table: Exploring Reinforcement Learning for Table Reasoningcited by 18
  4. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  5. 2024Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trendscited by 3
  6. 2025TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Modelscited by 3
  7. 2024Exploring Performance Contrasts in TableQA: Step-by-Step Reasoning Boosts Bigger Language Models, Limits Smaller Language Modelscited by 0