Language models have become ubiquitous in NLP and beyond. In particular, the new wave of large language models (LLMs) are increasingly used to communicate and solve practical problems in many languages and countries, and by an increasingly diverse set of users. However, even though there is no doubt that these models open up plenty of opportunities, there are important issues and research questions that arise when it comes to LLMs and their application in different languages and cultures. For instance, the language coverage in language models drastically decreases for less-resourced languages and as such, their performance. And not only the general performance is affected, but general-purpose LLMs may be implicitly biased to specific cultures and languages depending on their underlying training data.

In this talk, I will discuss how language models reflect on cultural diversity, including potential shortcomings and how language coverage and cultural awareness may be intrinsically intertwined. I will also share some lessons learned based on our recent research in this area, including a large effort to develop a cultural benchmark of everyday knowledge for dozens of languages and countries.