{"url":"/sota/question-answering-on-conditionalqa","task":{"name":"Question Answering","url":"/task/question-answering","note":null},"dataset":{"name":"ConditionalQA","url":"/dataset/conditionalqa"},"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":["Conditional (answers)","Conditional (w/ conditions)","Overall (answers)","Overall (w/ conditions)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Conditional (answers)":null,"Conditional (w/ conditions)":null,"Overall (answers)":null,"Overall (w/ conditions)":null}},"counts":{"rows":3,"rows_with_code":2,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"FiD","metrics":{"Conditional (answers)":"45.2 / 49.7","Conditional (w/ conditions)":"4.7 / 5.8","Overall (answers)":"44.4 / 50.8","Overall (w/ conditions)":"35.0 / 40.6"},"uses_additional_data":false,"paper_date":"2020-07-02","paper":"/paper/leveraging-passage-retrieval-with-generative","paper_url":"https://arxiv.org/abs/2007.01282v2","paper_title":"Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering","code":"https://github.com/jhyuklee/DensePhrases","n_code_links":8,"syntology":null},{"rank_in_archive_order":2,"model":"DocHopper","metrics":{"Conditional (answers)":"42.0 / 46.4","Conditional (w/ conditions)":"3.1 / 3.8","Overall (answers)":"40.6 / 45.2","Overall (w/ conditions)":"31.9 / 36.0"},"uses_additional_data":false,"paper_date":"2021-06-01","paper":"/paper/end-to-end-multihop-retrieval-for","paper_url":"https://arxiv.org/abs/2106.00200v2","paper_title":"Iterative Hierarchical Attention for Answering Complex Questions over Long Documents","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"ETC-Pipeline","metrics":{"Conditional (answers)":"39.4 / 41.8","Conditional (w/ conditions)":"2.5 / 3.4","Overall (answers)":"35.6 / 39.8","Overall (w/ conditions)":"26.9 / 30.8"},"uses_additional_data":false,"paper_date":"2020-04-17","paper":"/paper/etc-encoding-long-and-structured-data-in","paper_url":"https://arxiv.org/abs/2004.08483v5","paper_title":"ETC: Encoding Long and Structured Inputs in Transformers","code":"https://github.com/google-research/google-research/tree/master/etcmodel","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}}],"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":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1,"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"}}}