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Recent advancements in large language models have facilitated the executionof complex language tasks, not only in English but also in non-Englishlanguages. However, the tokenizers of most language models, such as Llama,trained on English-centric corpora, tend to excessively fragment tokens innon-English languages. This issue is especially pronounced in non-romanalphabetic languages, which are often divided at a character or even Unicodelevel, leading to slower text generation. To address this, our study introducesa novel framework designed to expedite text generation in these languages. Thisframework predicts larger linguistic units than those of conventionalmultilingual tokenizers and is specifically tailored to the target language,thereby reducing the number of decoding steps required. Our empirical resultsdemonstrate that the proposed framework increases the generation speed by afactor of 1.9 compared to standard decoding while maintaining the performanceof a pre-trained multilingual model on monolingual tasks.

A Simple Framework to Accelerate Multilingual Language Model for Monolingual Text Generation