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 this https URL

大型语言模型在全球范围或以英文为中心的主题上表现出广泛知识，但对于在网上不那么突出的其他文化相关主题表现如何仍存在疑问。通过引入英文和巴斯克文平行的选择题数据集 BertaQA，我们发现一流的大型语言模型在本地文化知识上表现出困难，但持续的巴斯克文预训练显著提高了英文查询下模型在巴斯克文化方面的表现，这是低资源语言向高资源语言的知识传递的首个确凿证据。我们的分析揭示了语言和知识之间复杂的相互作用，并发现某些先前的发现在重新评估地方主题时不完全成立。我们的数据集和评估代码可在此 https URL 下的开放许可下获取。