This Land is {Your, My} Land: Evaluating Geopolitical Biases in Language Models

Do the Spratly Islands belong to China, the Philippines, or Vietnam? A pretrained large language model (LLM) may answer differently if asked in the languages of each claimant country: Chinese, Tagalog, or Vietnamese. This contrasts with a multilingual human, who would likely answer consistently. In this work, we show that LLMs recall geopolitical knowledge inconsistently across languages -- a phenomenon we term geopolitical bias. As a targeted case study, we consider territorial disputes, inherently controversial and cross-lingual task. We first introduce the BorderLines dataset of territorial disputes. This covers 256 territories, each of which is associated to a set of multiple-choice questions in the languages of each claimant country (48 languages total). We then pose these questions to LLMs to probe their internal knowledge. Finally, we propose a suite of evaluation metrics based on accuracy, which compares responses with respect to the actual geopolitical situation, and consistency of the responses in different languages. These metrics allow us to quantify several findings, which include instruction-tuned LLMs underperforming base ones, and geopolitical bias being amplified in stronger models. We release our code and dataset to facilitate future investigation and mitigation of geopolitical bias.