  Multilingual language models are widely used to extend NLP systems to
low-resource languages. However, concrete evidence for the effects of
multilinguality on language modeling performance in individual languages
remains scarce. Here, we pre-train over 10,000 monolingual and multilingual
language models for over 250 languages, including multiple language families
that are under-studied in NLP. We assess how language modeling performance in
each language varies as a function of (1) monolingual dataset size, (2) added
multilingual dataset size, (3) linguistic similarity of the added languages,
and (4) model size (up to 45M parameters). We find that in moderation, adding
multilingual data improves low-resource language modeling performance, similar
to increasing low-resource dataset sizes by up to 33%. Improvements depend on
the syntactic similarity of the added multilingual data, with marginal
additional effects of vocabulary overlap. However, high-resource languages
consistently perform worse in multilingual pre-training scenarios. As dataset
sizes increase, adding multilingual data begins to hurt performance for both
low-resource and high-resource languages, likely due to limited model capacity
(the "curse of multilinguality"). These results suggest that massively
multilingual pre-training may not be optimal for any languages involved, but
that more targeted models can significantly improve performance.
