Geographical Erasure in Language Generation

• URL: http://arxiv.org/abs/2310.14777v1
• Date: Mon, 23 Oct 2023 10:26:14 GMT
• Title: Geographical Erasure in Language Generation
• Authors: Pola Schw\"obel, Jacek Golebiowski, Michele Donini, C\'edric Archambeau, Danish Pruthi
• Abstract summary: We study and operationalise a form of geographical erasure, wherein language models underpredict certain countries. We discover that erasure strongly correlates with low frequencies of country mentions in the training corpus. We mitigate erasure by finetuning using a custom objective.
• Score: 13.219867587151986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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