Codesota · Natural Language Processing · Table Question Answering · WikiTableQuestionsTasks/Natural Language Processing/Table Question Answering
Table Question Answering · benchmark dataset · 2015 · EN

WikiTableQuestions.

Question answering over Wikipedia tables requiring compositional reasoning

Submit a result ↵
§ 01 · Leaderboard

Best published scores.

3 results indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.

Primary
accuracy · higher is better
accuracy· primary
3 rows
#ModelOrgSubmittedPaper / codeaccuracy
01GPT-4OpenAIJan 2024arxiv75.30
02Claude 3.5 SonnetAnthropicJan 2025arxiv73
03TAPAS-largeOSSGoogleApr 2020TAPAS: Weakly Supervised Table Parsing via Pre-training48.70
Fig 2 · Rows sorted by score within each metric. Shaded row marks SOTA. Dates reflect model or paper release where available, otherwise the date Codesota accessed the source.
§ 03 · Progress

2 steps
of state of the art.

Each row below marks a model that broke the previous record on accuracy. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.

Higher scores win. Each subsequent entry improved upon the previous best.

SOTA line · accuracy
  1. Apr 6, 2020TAPAS-largeGoogle48.70
  2. Jan 1, 2024GPT-4OpenAI75.30
Fig 3 · SOTA-setting models only. 2 entries span Apr 2020 → Jan 2024.
§ 04 · Literature

1 paper
tied to this benchmark.

Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.

§ 06 · Contribute

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

Submit a result ↵Read submission guide
What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies