{"url":"/dataset/valueconsistency","name":"ValueConsistency","full_name":null,"description_markdown":"ValueConsistency is a dataset of both controversial and uncontroversial questions in English, Chinese, German, and Japanese for topics from the U.S., China, Germany, and Japan. It was generated via prompting by GPT-4 and validated manually.\r\n\r\nYou can find details about how we made the dataset in the linked paper and in our code base.\r\n\r\n    Curated by: Jared Moore, Tanvi Desphande, Diyi Yang\r\n    Language(s) (NLP): English, Chinese (Mandarin), German, Japanese\r\n    License: MIT","description_withheld":null,"homepage":"https://huggingface.co/datasets/jlcmoore/ValueConsistency","introduced_date":"2024-07-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/are-large-language-models-consistent-over","title":"Are Large Language Models Consistent over Value-laden Questions?","first_author":"Jared Moore","url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["ValueConsistency"],"data_loaders":[],"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."}