{"url":"/dataset/musicqa","name":"MusicQA","full_name":null,"description_markdown":"We propose the MusicQA dataset to train Music-enabled question-answering models and is used for training and evaluating our MU-LLaMA model. This dataset is generated using the MusicCaps and MagnaTagATune datasets. We utilize the descriptions/tags from existing datasets to prompt the MPT-7B Chat model to generate question-answer pairs through inference, reasoning, and paraphrasing. The dataset contains 12,542 music files for training making up 76.15 hours of music with 112,878 question-answer pairs.","description_withheld":null,"homepage":"https://huggingface.co/datasets/mu-llama/MusicQA","introduced_date":"2023-08-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/music-understanding-llama-advancing-text-to","title":"Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning","first_author":"Shansong Liu","url":null},"license":null,"modalities":[],"tasks":[{"name":"Music Question Answering","url":"/task/music-question-answering","datasets_with_task":"/datasets/task/music-question-answering"}],"languages":[],"variants":["MusicQA"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/music-question-answering-on-musicqa","task":"Music Question Answering","dataset_variant":"MusicQA","rows":3,"metrics":["BLEU","METEOR","ROUGE","BERT Score"],"first_row_in_archive_order":{"model":"MU-LLaMA","paper":"/paper/music-understanding-llama-advancing-text-to","metrics":{"BERT Score":"0.901","BLEU":"0.306","METEOR":"0.385","ROUGE":"0.466"},"code_links":[{"title":"shansongliu/M2UGen","url":"https://github.com/shansongliu/M2UGen"},{"title":"shansongliu/MU-LLaMA","url":"https://github.com/shansongliu/MU-LLaMA"},{"title":"crypto-code/mu-llama","url":"https://github.com/crypto-code/mu-llama"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/music-understanding-llama-advancing-text-to","title":"Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning","date":"2023-08-22","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/listen-think-and-understand","title":"Listen, Think, and Understand","date":"2023-05-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/llama-adapter-efficient-fine-tuning-of","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","date":"2023-03-28","rows_on_this_dataset":1,"code_links":7,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"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."}