{"url":"/dataset/quasar-t","name":"QUASAR-T","full_name":"QUestion Answering by Search And Reading – Trivia","description_markdown":"**QUASAR-T** is a large-scale dataset aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. It consists of 43,013 open-domain trivia questions and their answers obtained from various internet sources. ClueWeb09 serves as the background corpus for extracting these answers. The answers to these questions are free-form spans of text, though most are noun phrases.\r\n\r\nSource: [Quasar: Datasets for Question Answering by Search and Reading](https://paperswithcode.com/paper/quasar-datasets-for-question-answering-by/)\r\nImage Source: [Quasar: Datasets for Question Answering by Search and Reading](https://paperswithcode.com/paper/quasar-datasets-for-question-answering-by/)","description_withheld":null,"homepage":"https://github.com/bdhingra/quasar","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/quasar-datasets-for-question-answering-by","title":"Quasar: Datasets for Question Answering by Search and Reading","first_author":"Bhuwan Dhingra","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Open-Domain Question Answering","url":"/task/open-domain-question-answering","datasets_with_task":"/datasets/task/open-domain-question-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Quasar","Quasart-T","QUASAR-T"],"data_loaders":[{"repo":"https://github.com/bdhingra/quasar","url":"https://github.com/bdhingra/quasar","frameworks":[]}],"num_papers_in_archive":55,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/question-answering-on-quasart-t","task":"Question Answering","dataset_variant":"Quasart-T","rows":7,"metrics":["EM"],"first_row_in_archive_order":{"model":"Cluster-Former (#C=512)","paper":"/paper/cluster-former-clustering-based-sparse","metrics":{"EM":"54"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cluster-former-clustering-based-sparse","title":"Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding","date":"2020-09-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/reformer-the-efficient-transformer-1","title":"Reformer: The Efficient Transformer","date":"2020-01-13","rows_on_this_dataset":1,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":6,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-passage-bert-a-globally-normalized-bert","title":"Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering","date":"2019-08-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/190410509","title":"Generating Long Sequences with Sparse Transformers","date":"2019-04-23","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/densely-connected-attention-propagation-for","title":"Densely Connected Attention Propagation for Reading Comprehension","date":"2018-11-10","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/denoising-distantly-supervised-open-domain","title":"Denoising Distantly Supervised Open-Domain Question Answering","date":"2018-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/reading-wikipedia-to-answer-open-domain","title":"Reading Wikipedia to Answer Open-Domain Questions","date":"2017-03-31","rows_on_this_dataset":1,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":15,"samples_ran":12,"samples_unverified":3,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}