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
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 various cross-lingual vocabulary
adaptation methods on five generative LLMs (including monolingual and
multilingual models) across four typologically-diverse languages and four
natural language understanding tasks. We find that cross-lingual vocabulary
adaptation 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.

对五种生成式大型语言模型进行了实证研究，探讨了跨语言词汇适应方法对提高模型推理效率的有效性，发现跨语言词汇适应可大幅提升模型推理速度高达 271.5%，同时适应更平衡的多语种数据可以使下游性能接近原始模型。