When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages

Multilingual language models are widely used to extend NLP systems to lowresource 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 understudied 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. Multilingual language models have been a fixture of natural language processing (NLP) research nearly since the introduction of Transformer language models (Devlin et al., 2019; Conneau et al., 2020a). However, while multilingual language models produce strong results across many languages, multilingual pre-training work almost exclusively focuses on pre-training a small number of models with some fixed distribution over languages (e.g. Thus, it is largely unknown how different pre-training language distributions, such as different quantities of multilingual data or different selections of languages, affect multilingual language model performance. Fujinuma et al. (2022) vary the set of pre-training languages, but they consider only 14 variations of 14 languages, and they focus on cross-lingual transfer after English fine-tuning. Figure 1: Left: Map of the 252 languages used in our study.