LLark: A Multimodal Instruction-Following Language Model for Music
research work·research paper·active
Research work examining music/audio understanding.
Verified facts
| Official site | https://arxiv.org/abs/2310.07160 ↗ |
|---|---|
| Geography | Global |
| arxiv | 2310.07160 |
| venue | ICML camera-ready version |
| authors | Josh Gardner; Simon Durand; Daniel Stoller; Rachel M. Bittner |
| methods | language modeling |
| code urls | https://github.com/spotify-research/llark |
| demo urls | unknown |
| exact title | LLark: A Multimodal Instruction-Following Language Model for Music |
| project urls | https://bit.ly/llark |
| original title | unknown |
| citation counts | unknown |
| research topics | music/audio understanding |
| peer review status | venue or acceptance claim recorded in arXiv comment |
| disclosed conflicts | unknown |
| stated contribution | Presents or evaluates the system, method, benchmark, or analysis identified in the paper title. |
| us market scope basis | Included as a materially relevant public research artifact in the US-facing AI-music ecosystem; direct affiliation varies. |
| funding acknowledgements | unknown |
| affiliations at publication | Google Research |
| publication or preprint date | 2023-10-11 |
| abstract level neutral summary | The work studies music/audio understanding; methods and evaluation details are in the official abstract. |
| datasets benchmarks models tools used | unknown |
| correction withdrawal retraction status | No withdrawal marker observed in captured arXiv metadata. |
Current
| affiliated with | Google Research 2023-10-11 — now |
|---|
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