{"url":"/dataset/meddistractqa-nonliteral","name":"MedDistractQA-Nonliteral","full_name":null,"description_markdown":"Novel benchmark adapted from the MedQA, with confounding statements introduced within the question regarding an irrelevant clinical term used in a nonclinical context (e.g., The patient's Zodiac sign is Cancer).","description_withheld":null,"homepage":"","introduced_date":"2025-04-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/medical-large-language-models-are-easily","title":"Medical large language models are easily distracted","first_author":"Krithik Vishwanath","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["MedDistractQA-Nonliteral"],"data_loaders":[],"num_papers_in_archive":1,"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."}