{"url":"/dataset/cfq","name":"CFQ","full_name":"Compositional Freebase Questions","description_markdown":"A large and realistic natural language question answering dataset.\r\n\r\nSource: [Measuring Compositional Generalization: A Comprehensive Method on Realistic Data](/paper/measuring-compositional-generalization-a-1)","description_withheld":null,"homepage":"https://github.com/google-research/google-research/tree/master/cfq","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/measuring-compositional-generalization-a-1","title":"Measuring Compositional Generalization: A Comprehensive Method on Realistic Data","first_author":"Daniel Keysers","url":null},"license":null,"modalities":[],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Semantic Parsing","url":"/task/semantic-parsing","datasets_with_task":"/datasets/task/semantic-parsing"},{"name":"Structured Prediction","url":"/task/structured-prediction","datasets_with_task":"/datasets/task/structured-prediction"}],"languages":[],"variants":["CFQ"],"data_loaders":[{"repo":"https://github.com/google-research/google-research","url":"https://github.com/google-research/google-research","frameworks":["tf"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/google-research-datasets/cfq","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/cfq","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/cfq","frameworks":["tf","jax"]}],"num_papers_in_archive":66,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-parsing-on-cfq","task":"Semantic Parsing","dataset_variant":"CFQ","rows":5,"metrics":["Exact Match"],"first_row_in_archive_order":{"model":"Dynamic Least-to-Most Prompting","paper":"/paper/compositional-semantic-parsing-with-large","metrics":{"Exact Match":"95.0"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/compositional-semantic-parsing-with-large","title":"Compositional Semantic Parsing with Large Language Models","date":"2022-09-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-algebraic-recombination-for","title":"Learning Algebraic Recombination for Compositional Generalization","date":"2021-07-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/unlocking-compositional-generalization-in-pre","title":"Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations","date":"2021-04-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/hierarchical-poset-decoding-for-compositional","title":"Hierarchical Poset Decoding for Compositional Generalization in Language","date":"2020-10-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/measuring-compositional-generalization-a-1","title":"Measuring Compositional Generalization: A Comprehensive Method on Realistic Data","date":"2019-12-20","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"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."}