Don't Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information

• URL: http://arxiv.org/abs/2206.10744v1
• Date: Tue, 21 Jun 2022 21:38:25 GMT
• Title: Don't Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information
• Authors: Tomasz Limisiewicz and David Mare\v{c}ek
• Abstract summary: We focus on two types of such signals in English texts: factual gender information and gender bias. We aim to diminish the stereotypical bias in the representations while preserving the factual gender signal.
• Score: 4.391102490444539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The representations in large language models contain multiple types of gender information. We focus on two types of such signals in English texts: factual gender information, which is a grammatical or semantic property, and gender bias, which is the correlation between a word and specific gender. We can disentangle the model's embeddings and identify components encoding both types of information with probing. We aim to diminish the stereotypical bias in the representations while preserving the factual gender signal. Our filtering method shows that it is possible to decrease the bias of gender-neutral profession names without significant deterioration of language modeling capabilities. The findings can be applied to language generation to mitigate reliance on stereotypes while preserving gender agreement in coreferences.

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