Ascertain the factors driving performance differences across text-conditioned music generation models

Ascertain whether observed performance differences among text-conditioned music generation models arise primarily from the choice of generative modeling paradigm (e.g., auto-regressive decoding versus conditional flow matching/diffusion) or from confounding factors such as training data, latent representations, architecture design, and optimization procedures.

Background

Performance comparisons across existing text-conditioned music generation systems are confounded by variations in training datasets, latent representations, architectures, and optimization procedures. The authors emphasize that this makes it difficult to identify what fundamentally accounts for differences in outcomes across models.

Their paper aims to isolate the modeling paradigm as the primary variable, but they explicitly note that, in general, it is unclear what fundamentally drives performance differences across models.