gwkrsrch/music_avqa

Music-AVQA (test)

The 9,185 items of the Music-AVQA test split (Li et al., CVPR 2022), in the order used for the evaluation in the paper below.

Released with Do Modern Video-LLMs Need to Listen? A Benchmark Audit and Scalable Remedy (Interspeech 2026, arXiv:2509.17901); code: naver-ai/unimambamia-av.

What is (and is not) in this dataset

This dataset contains item identifiers and values we produced (single-frame probe outputs and the like). It does not contain any question, option or answer text of Music-AVQA, nor any video. Those remain with the original release and its terms: the evaluation code of UniMambaMia-AV downloads the official annotation file at a fixed commit (data/json/avqa-test.json of GeWu-Lab/MUSIC-AVQA), checks its SHA-256 and joins it onto these rows (UniMambaMia/official_annotations.py). Videos have to be obtained from the Music-AVQA project.

Row order is fixed, so the doc_ids in benchmark_audit/single_frame_filter.json of the repository refer to the rows of this dataset in order.

Columns

Column	Description
source_index	0-based position of the item in the official avqa-test.json (the join key; question_id alone is not unique in that file).
question_id	question_id of that item, kept for reference.

Usage

The lmms-eval task music_avqa of the repository loads this dataset and joins the official annotations automatically:

bash eval_unimambamia.sh gwkrsrch/UniMambaMia-AV-Qwen2-7B --tasks music_avqa

To use it outside lmms-eval:

import datasets
from official_annotations import attach_music_avqa  # UniMambaMia/official_annotations.py

ds = attach_music_avqa(datasets.load_dataset("gwkrsrch/music_avqa", split="test"))

License

The columns of this dataset are released under the MIT License. Music-AVQA itself (annotations and videos) is not part of this dataset and is subject to the terms of its original release.

Citation

If you use this dataset, please cite our paper and the original Music-AVQA paper.

@inproceedings{kim2026doesaudiomatter,
  title     = {Do Modern Video-LLMs Need to Listen? A Benchmark Audit and Scalable Remedy},
  author    = {Geewook Kim and Minjoon Seo},
  booktitle = {Proc. Interspeech 2026},
  year      = {2026},
  url       = {https://arxiv.org/abs/2509.17901}
}

@inproceedings{li2022learning,
  title     = {Learning to Answer Questions in Dynamic Audio-Visual Scenarios},
  author    = {Li, Guangyao and Wei, Yake and Tian, Yapeng and Xu, Chenliang and Wen, Ji-Rong and Hu, Di},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2022}
}