  Multilingual pretraining has been a successful solution to the challenges
posed by the lack of resources for languages. These models can transfer
knowledge to target languages with minimal or no examples. Recent research
suggests that monolingual models also have a similar capability, but the
mechanisms behind this transfer remain unclear. Some studies have explored
factors like language contamination and syntactic similarity. An emerging line
of research suggests that the representations learned by language models
contain two components: a language-specific and a language-agnostic component.
The latter is responsible for transferring a more universal knowledge. However,
there is a lack of comprehensive exploration of these properties across diverse
target languages. To investigate this hypothesis, we conducted an experiment
inspired by the work on the Scaling Laws for Transfer. We measured the amount
of data transferred from a source language to a target language and found that
models initialized from diverse languages perform similarly to a target
language in a cross-lingual setting. This was surprising because the amount of
data transferred to 10 diverse target languages, such as Spanish, Korean, and
Finnish, was quite similar. We also found evidence that this transfer is not
related to language contamination or language proximity, which strengthens the
hypothesis that the model also relies on language-agnostic knowledge. Our
experiments have opened up new possibilities for measuring how much data
represents the language-agnostic representations learned during pretraining.
