{"url":"/dataset/qed","name":"QED","full_name":null,"description_markdown":"**QED** is a linguistically principled framework for explanations in question answering. Given a question and a passage, QED represents an explanation of the answer as a combination of discrete, human-interpretable steps:\nsentence selection := identification of a sentence implying an answer to the question\nreferential equality := identification of noun phrases in the question and the answer sentence that refer to the same thing\npredicate entailment := confirmation that the predicate in the sentence entails the predicate in the question once referential equalities are abstracted away.\nThe QED dataset is an expert-annotated dataset of QED explanations build upon a subset of the Google Natural Questions dataset.\n\nSource: [https://github.com/google-research-datasets/QED](https://github.com/google-research-datasets/QED)\nImage Source: [https://github.com/google-research-datasets/QED](https://github.com/google-research-datasets/QED)","description_withheld":null,"homepage":"https://github.com/google-research-datasets/QED","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/qed-a-framework-and-dataset-for-explanations","title":"QED: A Framework and Dataset for Explanations in Question Answering","first_author":"Matthew Lamm","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Drug Discovery","url":"/task/drug-discovery","datasets_with_task":"/datasets/task/drug-discovery"}],"languages":[],"variants":["QED"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/google-research-datasets/qed","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/qed","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/google-research-datasets/QED","url":"https://github.com/google-research-datasets/QED","frameworks":[]}],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/drug-discovery-on-qed","task":"Drug Discovery","dataset_variant":"QED","rows":1,"metrics":["Diversity","Success"],"first_row_in_archive_order":{"model":"HierG2G","paper":"/paper/multi-resolution-autoregressive-graph-to","metrics":{"Diversity":"0.477","Success":"76.9%"},"code_links":[{"title":"wengong-jin/hgraph2graph","url":"https://github.com/wengong-jin/hgraph2graph"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/multi-resolution-autoregressive-graph-to","title":"Hierarchical Graph-to-Graph Translation for Molecules","date":"2019-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"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."}