{"url":"/sota/natural-language-inference-on-tabfact","task":{"name":"Natural Language Inference","url":"/task/natural-language-inference","note":null},"dataset":{"name":"TabFact","url":"/dataset/tabfact"},"category":"Natural Language Processing","categories":["Natural Language Processing","Reasoning"],"category_note":null,"description":"**Natural language inference (NLI)** is the task of determining whether a \"hypothesis\" is \r\ntrue (entailment), false (contradiction), or undetermined (neutral) given a \"premise\".\r\n\r\nExample:\r\n\r\n| Premise | Label | Hypothesis |\r\n| --- | ---| --- |\r\n| A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |\r\n| An older and younger man smiling. | neutral  | Two men are smiling and laughing at the cats playing on the floor. |\r\n| A soccer game with multiple males playing. | entailment | Some men are playing a sport. |\r\n\r\nApproaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include [SNLI](/dataset/snli), [MultiNLI](/dataset/multinli), [SciTail](/dataset/scitail), among others. You can get hands-on practice on the SNLI task by following this [d2l.ai chapter](https://d2l.ai/chapter_natural-language-processing-applications/natural-language-inference-and-dataset.html).\r\n\r\nFurther readings:\r\n\r\n- [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172)","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"],"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"}},"counts":{"rows":1,"rows_with_code":0,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ChatGPT 3.5 SpatialFormat","metrics":{"Accuracy":"70.1"},"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}],"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}