{"url":"/dataset/quite","name":"QUITE","full_name":"Quantifying Uncertainty in natural language Text","description_markdown":"QUITE (Quantifying Uncertainty in natural language Text) is an entirely new benchmark that allows for assessing the capabilities of neural language model-based systems w.r.t. to Bayesian reasoning on a large set of input text that describes probabilistic relationships in natural language text.","description_withheld":null,"homepage":"https://huggingface.co/datasets/timo-pierre-schrader/QUITE","introduced_date":"2024-10-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/quite-quantifying-uncertainty-in-natural","title":"QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios","first_author":"Timo Pierre Schrader","url":null},"license":{"name":"cc-by-4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Mathematical Reasoning","url":"/task/mathematical-reasoning","datasets_with_task":"/datasets/task/mathematical-reasoning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["QUITE"],"data_loaders":[{"repo":"https://github.com/boschresearch/quite-emnlp24","url":"https://github.com/boschresearch/quite-emnlp24","frameworks":[]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}