  Large language models (LLMs) encode vast amounts of world knowledge. However,
since these models are trained on large swaths of internet data, they are at
risk of inordinately capturing information about dominant groups. This
imbalance can propagate into generated language. In this work, we study and
operationalise a form of geographical erasure, wherein language models
underpredict certain countries. We demonstrate consistent instances of erasure
across a range of LLMs. We discover that erasure strongly correlates with low
frequencies of country mentions in the training corpus. Lastly, we mitigate
erasure by finetuning using a custom objective.
