Multilingual Language Models

Multilingual language models study how systems can understand, reason, translate, and generate text across languages, including those with limited training data or distinct scripts. Common approaches include multilingual pretraining and data balancing, continued pretraining and instruction tuning, language-aware tokenization, and transfer methods such as shared representations, adapters, and mixture-of-experts models. Researchers evaluate performance with translation, comprehension, reasoning, and safety benchmarks, while probing internal representations and using inference-time steering to improve language control. A central tension is how to expand language coverage and transfer shared knowledge without degrading performance, reinforcing English or high-resource-language biases, or overlooking differences between languages and scripts.