8. Broader Impact
MusicLM generates high-quality music based on a text de-
scription, and thus it further extends the set of tools that as-
sist humans with creative music tasks. However, there are
several risks associated with our model and the use-case
it tackles. The generated samples will reﬂect the biases
present in the training data, raising the question about ap-
propriateness for music generation for cultures underrepre-
sented in the training data, while at the same time also rais-
ing concerns about cultural appropriation.
We acknowledge the risk of potential misappropriation of
creative content associated to the use-case. In accordance
with responsible model development practices, we con-
ducted a thorough study of memorization, adapting and
extending a methodology used in the context of text-based
LLMs, focusing on the semantic modeling stage. We found
that only a tiny fraction of examples was memorized ex-
actly, while for 1% of the examples we could identify an ap-
MusicLM