Large language models have advanced the state of the art in natural language processing.
However, their predominant design for English or a limited set of languages creates a …

Despite rapid progress in large language models (LLMs), their performance on a vast
majority of languages remain unsatisfactory. In this paper, we study building language …

We investigate continued pretraining of LLMs for language adaptation on a tight academic
budget: a setting in which only a few GPUs can be used in parallel, for a heavily constrained …

Large language models (LLMs) have transformed natural language processing. Yet, their
predominantly English-centric training has led to biases and performance disparities across …

Vocabulary adaptation, which integrates new vocabulary into pre-trained language models
(LMs), enables expansion to new languages and mitigates token over-fragmentation …

While large language models (LLMs) continue to grow in scale and gain new zero-shot
capabilities, their performance for languages beyond English increasingly lags behind. This …

Large language models (LLMs) have revolutionized natural language processing, but their
predominant focus on English has resulted in biases and performance differences across …