Geographic and Geopolitical Biases of Language Models

Fahim Faisal; Antonios Anastasopoulos

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arXiv · 2212.10408

Geographic and Geopolitical Biases of Language Models

Fahim Faisal·Antonios Anastasopoulos

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Abstract

Pretrained language models (PLMs) often fail to fairly represent target users from certain world regions because of the under-representation of those regions in training datasets. With recent PLMs trained on enormous data sources, quantifying their potential biases is difficult, due to their black-box nature and the sheer scale of the data sources. In this work, we devise an approach to study the geographic bias (and knowledge) present in PLMs, proposing a Geographic-Representation Probing Framework adopting a self-conditioning method coupled with entity-country mappings. Our findings suggest PLMs' representations map surprisingly well to the physical world in terms of country-to-country associations, but this knowledge is unequally shared across languages. Last, we explain how large PLMs despite exhibiting notions of geographical proximity, over-amplify geopolitical favouritism at inference time.

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Fahim Faisal, Antonios Anastasopoulos. 2022-12-20. Geographic and Geopolitical Biases of Language Models. https://arxiv.org/abs/2212.10408

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