Papers › Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering...
Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning
Shansong Liu, Atin Sakkeer Hussain, Chenshuo Sun, Ying Shan
Text-to-music generation (T2M-Gen) faces a major obstacle due to the scarcity of large-scale publicly available music datasets with natural language captions. To address this, we propose the Music Understanding LLaMA (MU-LLaMA), capable of answering music-related questions and generating captions for music files. Our model utilizes audio representations from a pretrained MERT model to extract music features. However, obtaining a suitable dataset for training the MU-LLaMA model remains challenging, as existing publicly accessible audio question answering datasets lack the necessary depth for open-ended music question answering. To fill this gap, we present a methodology for generating question-answer pairs from existing audio captioning datasets and introduce the MusicQA Dataset designed for answering open-ended music-related questions. The experiments demonstrate that the proposed MU-LLaMA model, trained on our designed MusicQA dataset, achieves outstanding performance in both music question answering and music caption generation across various metrics, outperforming current state-of-the-art (SOTA) models in both fields and offering a promising advancement in the T2M-Gen research field.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Music Question Answering | MusicQA | MU-LLaMA | BERT Score | 0.901 | #1 of 3 | Archive leaderboard | report |
| Music Question Answering | MusicQA | MU-LLaMA | BLEU | 0.306 | #1 of 3 | Archive leaderboard | report |
| Music Question Answering | MusicQA | MU-LLaMA | METEOR | 0.385 | #1 of 3 | Archive leaderboard | report |
| Music Question Answering | MusicQA | MU-LLaMA | ROUGE | 0.466 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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