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WikiTableQuestions

Introduced by Panupong Pasupat et al. in Compositional Semantic Parsing on Semi-Structured Tables1 Jan 2015 archive 2025-07-28

WikiTableQuestions is a question answering dataset over semi-structured tables. It is comprised of question-answer pairs on HTML tables, and was constructed by selecting data tables from Wikipedia that contained at least 8 rows and 5 columns. Amazon Mechanical Turk workers were then tasked with writing trivia questions about each table. WikiTableQuestions contains 22,033 questions. The questions were not designed by predefined templates but were hand crafted by users, demonstrating high linguistic variance. Compared to previous datasets on knowledge bases it covers nearly 4,000 unique column headers, containing far more relations than closed domain datasets and datasets for querying knowledge bases. Its questions cover a wide range of domains, requiring operations such as table lookup, aggregation, superlatives (argmax, argmin), arithmetic operations, joins and unions.

Source: Explaining Queries over Web Tables to Non-Experts Image Source: https://ppasupat.github.io/WikiTableQuestions/

HomepageBenchmarksPapers JSON

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Semantic Parsing WikiTableQuestions ARTEMIS-DA Accuracy (Test) 80.8 ARTEMIS-DA: An Advanced Reasoning and Transformation... — 22 Compare
Question Answering WikiTableQuestions ChatGPT 3.5 SpatialFormat Accuracy 47.7 LAPDoc: Layout-Aware Prompting for Documents — 2 Compare

Papers archive 2025-07-28

20 shown of 20 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 79. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics 0 1 18 Dec 2024 not harvested
Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering 1 1 10 Oct 2024 not harvested
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA 1 3 25 Sep 2024 not harvested
NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization 1 2 25 Jun 2024 not harvested
Efficient Prompting for LLM-based Generative Internet of Things 0 1 14 Jun 2024 not harvested
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition 2 2 15 Apr 2024 ran 7 of 15 samples (8 unverified; 15 pointer-only for licence)
LAPDoc: Layout-Aware Prompting for Documents 0 1 15 Feb 2024 not harvested
CABINET: Content Relevance based Noise Reduction for Table Question Answering 1 1 2 Feb 2024 ran 3 of 5 samples (2 unverified; 5 pointer-only for licence)
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding 2 1 9 Jan 2024 ran 6 of 8 samples (2 unverified)
Rethinking Tabular Data Understanding with Large Language Models 1 1 27 Dec 2023 ran 10 of 11 samples (1 unverified)
LEVER: Learning to Verify Language-to-Code Generation with Execution 1 1 16 Feb 2023 ran 6 of 22 samples (16 unverified)
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning 2 1 31 Jan 2023 not harvested
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples 1 1 22 Oct 2022 ran 0 of 11 samples (11 unverified)
Binding Language Models in Symbolic Languages 4 1 6 Oct 2022 ran 2 of 3 samples (1 unverified)
OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering 1 1 8 Jul 2022 ran 1 of 2 samples (1 unverified; 2 pointer-only for licence)
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models 1 1 16 Jan 2022 ran 1 of 5 samples (4 unverified)
TAPEX: Table Pre-training via Learning a Neural SQL Executor 4 1 16 Jul 2021 not harvested
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data 1 1 17 May 2020 not harvested
TAPAS: Weakly Supervised Table Parsing via Pre-training 8 1 5 Apr 2020 ran 0 of 14 samples (14 unverified)
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs 1 1 9 Sep 2019 not harvested

Dataset loaders archive 2025-07-28

www.tensorflow.org/datasets/catalog/wiki_table_questionstensorflow/datasetstfjax

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Question AnsweringSemantic Parsing

License archive 2025-07-28

CC-BY-SA-4.0

Modalities archive 2025-07-28

TextsTabular

Languages archive 2025-07-28

English

Variants archive 2025-07-28

  • WikiTableQuestions

1 variant name, as the archive lists them.

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