  The development of state-of-the-art generative large language models (LLMs)
disproportionately relies on English-centric tokenizers, vocabulary and
pre-training data. Despite the fact that some LLMs have multilingual
capabilities, recent studies have shown that their inference efficiency
deteriorates when generating text in languages other than English. This results
in increased inference time and costs. Cross-lingual vocabulary adaptation
(CVA) methods have been proposed for adapting models to a target language
aiming to improve downstream performance. However, the effectiveness of these
methods on increasing inference efficiency of generative LLMs has yet to be
explored. In this paper, we perform an empirical study of five CVA methods on
four generative LLMs (including monolingual and multilingual models) across
four typologically-diverse languages and four natural language understanding
tasks. We find that CVA substantially contributes to LLM inference speedups of
up to 271.5\%. We also show that adapting LLMs that have been pre-trained on
more balanced multilingual data results in downstream performance comparable to
the original models.
