  Recent language models have shown impressive multilingual performance, even
when not explicitly trained for it. Despite this, there are concerns about the
quality of their outputs across different languages. In this paper, we show how
disparity in the treatment of different languages arises at the tokenization
stage, well before a model is even invoked. The same text translated into
different languages can have drastically different tokenization lengths, with
differences up to 15 times in some cases. These disparities persist even for
tokenizers that are intentionally trained for multilingual support.
Character-level and byte-level models also exhibit over 4 times the difference
in the encoding length for some language pairs. This induces unfair treatment
for some language communities in regard to the cost of accessing commercial
language services, the processing time and latency, as well as the amount of
content that can be provided as context to the models. Therefore, we make the
case that we should train future language models using multilingually fair
subword tokenizers.
