  Fine-tuning large language models for multilingual downstream tasks requires
a diverse set of languages to capture the nuances and structures of different
linguistic contexts effectively. While the specific number varies depending on
the desired scope and target languages, we argue that the number of languages,
language exposure, and similarity that incorporate the selection of languages
for fine-tuning are some important aspects to examine. By fine-tuning large
multilingual models on 1 to 52 languages, this paper answers one question: How
many languages are needed in instruction fine-tuning for multilingual tasks? We
investigate how multilingual instruction fine-tuned models behave on
multilingual benchmarks with an increasing number of languages and discuss our
findings from the perspective of language exposure and similarity.
