  Large Language Models (LLMs), trained predominantly on extensive English
data, often exhibit limitations when applied to other languages. Current
research is primarily focused on enhancing the multilingual capabilities of
these models by employing various tuning strategies. Despite their
effectiveness in certain languages, the understanding of the multilingual
abilities of LLMs remains incomplete. This study endeavors to evaluate the
multilingual capacity of LLMs by conducting an exhaustive analysis across 101
languages, and classifies languages with similar characteristics into four
distinct quadrants. By delving into each quadrant, we shed light on the
rationale behind their categorization and offer actionable guidelines for
tuning these languages. Extensive experiments reveal that existing LLMs possess
multilingual capabilities that surpass our expectations, and we can
significantly improve the multilingual performance of LLMs by focusing on these
distinct attributes present in each quadrant.

Computation and Language;Artificial Intelligence