{"url":"/sota/question-answering-on-wikitablequestions","task":{"name":"Question Answering","url":"/task/question-answering","note":null},"dataset":{"name":"WikiTableQuestions","url":"/dataset/wikitablequestions"},"category":"Natural Language Processing","categories":["Miscellaneous","Natural Language Processing","Reasoning"],"category_note":null,"description":"Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include [SQuAD](/dataset/squad), [HotPotQA](/dataset/hotpotqa), [bAbI](/dataset/babi-1), [TriviaQA](/dataset/triviaqa), [WikiQA](/dataset/wikiqa), and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.\r\n\r\n( Image credit: [SQuAD](https://rajpurkar.github.io/mlx/qa-and-squad/) )","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy","Accuracy (Test)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher","Accuracy (Test)":"higher"}},"counts":{"rows":2,"rows_with_code":1,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ChatGPT 3.5 SpatialFormat","metrics":{"Accuracy":"47.7"},"uses_additional_data":false,"paper_date":"2024-02-15","paper":"/paper/lapdoc-layout-aware-prompting-for-documents","paper_url":"https://arxiv.org/abs/2402.09841v1","paper_title":"LAPDoc: Layout-Aware Prompting for Documents","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"TabSQLify (col+row)","metrics":{"Accuracy (Test)":"64.7"},"uses_additional_data":false,"paper_date":"2024-04-15","paper":"/paper/tabsqlify-enhancing-reasoning-capabilities-of","paper_url":"https://arxiv.org/abs/2404.10150v1","paper_title":"TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition","code":"https://github.com/mahadi-nahid/tabsqlify","n_code_links":2,"syntology":{"n_ran":7,"n_unverified":8,"n_samples":15,"n_pointer_only_licence":15}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":7,"n_unverified":8,"n_samples":15,"n_pointer_only_licence":15,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":7,"n_unverified":8,"n_samples":15,"n_pointer_only_licence":15,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}