  Large Language Models (LLMs) are prone to factuality hallucination,
generating text that contradicts established knowledge. While extensive
research has addressed this in English, little is known about multilingual
LLMs. This paper systematically evaluates multilingual LLMs' factual accuracy
across languages and geographic regions. We introduce a novel pipeline for
multilingual factuality evaluation, adapting FActScore(Min et al., 2023) for
diverse languages. Our analysis across nine languages reveals that English
consistently outperforms others in factual accuracy and quantity of generated
facts. Furthermore, multilingual models demonstrate a bias towards factual
information from Western continents. These findings highlight the need for
improved multilingual factuality assessment and underscore geographical biases
in LLMs' fact generation.
