  Recent studies have highlighted the presence of cultural biases in Large
Language Models (LLMs), yet often lack a robust methodology to dissect these
phenomena comprehensively. Our work aims to bridge this gap by delving into the
Food domain, a universally relevant yet culturally diverse aspect of human
life. We introduce FmLAMA, a multilingual dataset centered on food-related
cultural facts and variations in food practices. We analyze LLMs across various
architectures and configurations, evaluating their performance in both
monolingual and multilingual settings. By leveraging templates in six different
languages, we investigate how LLMs interact with language-specific and cultural
knowledge. Our findings reveal that (1) LLMs demonstrate a pronounced bias
towards food knowledge prevalent in the United States; (2) Incorporating
relevant cultural context significantly improves LLMs' ability to access
cultural knowledge; (3) The efficacy of LLMs in capturing cultural nuances is
highly dependent on the interplay between the probing language, the specific
model architecture, and the cultural context in question. This research
underscores the complexity of integrating cultural understanding into LLMs and
emphasizes the importance of culturally diverse datasets to mitigate biases and
enhance model performance across different cultural domains.
