{"url":"/dataset/reclor","name":"ReClor","full_name":null,"description_markdown":"Logical reasoning is an important ability to examine, analyze, and critically evaluate arguments as they occur in ordinary language as the definition from Law School Admission Council. **ReClor** is a dataset extracted from logical reasoning questions of standardized graduate admission examinations.\r\n\r\nSource: [ReClor](https://whyu.me/reclor/)","description_withheld":null,"homepage":"https://whyu.me/reclor/","introduced_date":"2020-02-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/reclor-a-reading-comprehension-dataset-1","title":"ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning","first_author":"Weihao Yu","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Reading Comprehension","url":"/task/reading-comprehension","datasets_with_task":"/datasets/task/reading-comprehension"},{"name":"Machine Reading Comprehension","url":"/task/machine-reading-comprehension","datasets_with_task":"/datasets/task/machine-reading-comprehension"},{"name":"Logical Reasoning Question Answering","url":"/task/logical-reasoning-question-ansering","datasets_with_task":"/datasets/task/logical-reasoning-question-ansering"}],"languages":[],"variants":["ReClor"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/community-datasets/reclor","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/reclor","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":88,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/reading-comprehension-on-reclor","task":"Reading Comprehension","dataset_variant":"ReClor","rows":39,"metrics":["Test"],"first_row_in_archive_order":{"model":"Rational Reasoner / IDOL","paper":"/paper/idol-indicator-oriented-logic-pre-training","metrics":{"Test":"80.6"},"code_links":[{"title":"GeekDream-x/IDOL","url":"https://github.com/GeekDream-x/IDOL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/logical-reasoning-question-ansering-on-reclor","task":"Logical Reasoning Question Answering","dataset_variant":"ReClor","rows":3,"metrics":["Accuracy","Accuracy (easy)","Accuracy (hard)"],"first_row_in_archive_order":{"model":"XLNet-large","paper":"/paper/reclor-a-reading-comprehension-dataset-1","metrics":{"Accuracy":"56.0","Accuracy (easy)":"75.7","Accuracy (hard)":"40.5"},"code_links":[{"title":"yuweihao/reclor","url":"https://github.com/yuweihao/reclor"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/machine-reading-comprehension-on-reclor","task":"Machine Reading Comprehension","dataset_variant":"ReClor","rows":3,"metrics":["Accuracy","Accuracy (easy)","Accuracy (hard)"],"first_row_in_archive_order":{"model":"XLNet-large","paper":"/paper/reclor-a-reading-comprehension-dataset-1","metrics":{"Accuracy":"56.0","Accuracy (easy)":"75.7","Accuracy (hard)":"40.5"},"code_links":[{"title":"yuweihao/reclor","url":"https://github.com/yuweihao/reclor"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/question-answering-on-reclor","task":"Question Answering","dataset_variant":"ReClor","rows":3,"metrics":["Accuracy","Accuracy (easy)","Accuracy (hard)"],"first_row_in_archive_order":{"model":"XLNet-large","paper":"/paper/reclor-a-reading-comprehension-dataset-1","metrics":{"Accuracy":"56.0","Accuracy (easy)":"75.7","Accuracy (hard)":"40.5"},"code_links":[{"title":"yuweihao/reclor","url":"https://github.com/yuweihao/reclor"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/idol-indicator-oriented-logic-pre-training","title":"IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning","date":"2023-06-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/logiformer-a-two-branch-graph-transformer","title":"Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning","date":"2022-05-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/answer-uncertainty-and-unanswerability-in-1","title":"Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension","date":"2022-05-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/merit-meta-path-guided-contrastive-learning","title":"MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning","date":"2022-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fact-driven-logical-reasoning","title":"Fact-driven Logical Reasoning for Machine Reading Comprehension","date":"2021-05-21","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/logic-driven-context-extension-and-data","title":"Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text","date":"2021-05-08","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/dagn-discourse-aware-graph-network-for","title":"DAGN: Discourse-Aware Graph Network for Logical Reasoning","date":"2021-03-26","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/reclor-a-reading-comprehension-dataset-1","title":"ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning","date":"2020-02-11","rows_on_this_dataset":13,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":2,"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":14,"samples_ran":7,"samples_unverified":7,"pointer_only_for_licence":7,"papers_with_no_sample_that_ran":1,"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."}