  Large Language Models (LLMs) exhibit extensive knowledge about the world, but
most evaluations have been limited to global or anglocentric subjects. This
raises the question of how well these models perform on topics relevant to
other cultures, whose presence on the web is not that prominent. To address
this gap, we introduce BertaQA, a multiple-choice trivia dataset that is
parallel in English and Basque. The dataset consists of a local subset with
questions pertinent to the Basque culture, and a global subset with questions
of broader interest. We find that state-of-the-art LLMs struggle with local
cultural knowledge, even as they excel on global topics. However, we show that
continued pre-training in Basque significantly improves the models' performance
on Basque culture, even when queried in English. To our knowledge, this is the
first solid evidence of knowledge transfer from a low-resource to a
high-resource language. Our analysis sheds light on the complex interplay
between language and knowledge, and reveals that some prior findings do not
fully hold when reassessed on local topics. Our dataset and evaluation code are
available under open licenses at https://github.com/juletx/BertaQA.
