mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
This addresses the problem of improving multilingual NLP performance for a wide range of languages, including low-resource ones, though it appears incremental as it builds on existing encoder models with novel training techniques.
The authors tackled the lack of recent research on multilingual encoder models by introducing mmBERT, pretrained on 3T tokens across over 1800 languages, which significantly outperforms previous models on classification and retrieval tasks for both high and low-resource languages, achieving performance similar to models like OpenAI's o3 and Google's Gemini 2.5 Pro.
Encoder-only languages models are frequently used for a variety of standard machine learning tasks, including classification and retrieval. However, there has been a lack of recent research for encoder models, especially with respect to multilingual models. We introduce mmBERT, an encoder-only language model pretrained on 3T tokens of multilingual text in over 1800 languages. To build mmBERT we introduce several novel elements, including an inverse mask ratio schedule and an inverse temperature sampling ratio. We add over 1700 low-resource languages to the data mix only during the decay phase, showing that it boosts performance dramatically and maximizes the gains from the relatively small amount of training data. Despite only including these low-resource languages in the short decay phase we achieve similar classification performance to models like OpenAI's o3 and Google's Gemini 2.5 Pro. Overall, we show that mmBERT significantly outperforms the previous generation of models on classification and retrieval tasks -- on both high and low-resource languages.