{"url":"/dataset/mmcomposition","name":"MMComposition","full_name":null,"description_markdown":"MMCOMPOSITION is a high-quality benchmark specifically designed to comprehensively evaluate the compositionality of pre-trained Vision-Language Models (VLMs) across three main dimensions—VL compositional perception, reasoning, and probing—which are further divided into 13 distinct categories of questions. While previous benchmarks have mainly focused on text-to-image retrieval, single-choice questions, and open-ended text generation, MMCOMPOSITION introduces a more diverse and challenging set of 4,342 tasks covering both single-image and multi-image scenarios, as well as single-choice and indefinite-choice formats. This expanded range of tasks aims to capture the complex interplay between vision and language more effectively, surpassing earlier benchmarks such as ARO and Winoground by providing a more comprehensive and in-depth assessment of models’ cross-modal compositional capabilities.","description_withheld":null,"homepage":"https://hanghuacs.github.io/MMComposition/","introduced_date":"2024-10-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/mmcomposition-revisiting-the-compositionality","title":"MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models","first_author":"Hang Hua","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MMComposition"],"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."}