{"url":"/dataset/mminstruct-gpt4v","name":"MMInstruct-GPT4V","full_name":"MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity","description_markdown":"Vision-language supervised fine-tuning effectively enhances VLLM performance, but existing visual instruction tuning datasets have limitations:\r\n\r\n1. **Instruction Annotation Quality**: Despite strong performance, advanced VLLMs may generate instructions with inaccuracies, such as hallucinations.\r\n2. **Instruction and Image Diversity**: Limited instruction types and lack of diverse image data impact the model's ability to generate varied and realistic outputs.\r\n\r\n\r\nMMInstruct Dataset\r\n\r\nTo address these challenges, we created the MMInstruct dataset, featuring:\r\n- **973K instructions** from **24 domains**\r\n- Four instruction types: Judgement, Multiple-Choice, Long Visual Question Answering, and Short Visual Question Answering.","description_withheld":null,"homepage":"https://huggingface.co/datasets/yuecao0119/MMInstruct-GPT4V","introduced_date":"2024-07-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/mminstruct-a-high-quality-multi-modal","title":"MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity","first_author":"Yangzhou Liu","url":null},"license":{"name":"Apache License, Version 2.0","url":"https://opensource.org/license/apache-2-0"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Visual Question Answering","url":"/task/visual-question-answering-1","datasets_with_task":"/datasets/task/visual-question-answering-1"},{"name":"Image Captioning","url":"/task/image-captioning","datasets_with_task":"/datasets/task/image-captioning"},{"name":"Multimodal Deep Learning","url":"/task/multimodal-deep-learning","datasets_with_task":"/datasets/task/multimodal-deep-learning"},{"name":"Multiple-choice","url":"/task/multiple-choice","datasets_with_task":"/datasets/task/multiple-choice"},{"name":"Long Form Question Answering","url":"/task/long-form-question-answering","datasets_with_task":"/datasets/task/long-form-question-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["MMInstruct-GPT4V"],"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."}