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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10457041" target="_blank" >RIV/00216208:11320/22:10457041 - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.gebnlp-1.3.pdf" target="_blank" >https://aclanthology.org/2022.gebnlp-1.3.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

  • ISBN

    978-1-955917-68-1

  • ISSN

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    17-29

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

    Stroudsburg, PA, USA

  • Event location

    Seattle, WA, USA

  • Event date

    Sep 15, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article