{"url":"/dataset/medlfqa","name":"MedLFQA","full_name":"Medical Long-form Question Answering","description_markdown":"MedLFQA is reconstructed by reformulating the current four biomedical long-form question-answering benchmark datasets: LiveQA, MedicationQA, HealthsearchQA, and K-QA.\r\nMedLFQA consists of four components: question (Q), answer (A), must-have statements (MH), and nice-to-have statements (NH).\r\nIt facilitates the automatic evaluation of models' responses and provides a comprehensive understanding of how the model responds to a patient's question.","description_withheld":null,"homepage":"https://huggingface.co/datasets/dmis-lab/MedLFQA","introduced_date":"2024-05-21","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["MedLFQA"],"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."}