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        <title>Evaluating Gender Bias in Dutch NLP: Insights from RobBERT-2023 and the HONEST Framework</title>
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            <title>Proceedings of the 3rd Workshop on Gender-Inclusive Translation Technologies (GITT 2025)</title>
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            <namePart type="given">Janiça</namePart>
            <namePart type="family">Hackenbuchner</namePart>
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            <namePart type="given">Luisa</namePart>
            <namePart type="family">Bentivogli</namePart>
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            <namePart type="given">Joke</namePart>
            <namePart type="family">Daems</namePart>
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        <name type="personal">
            <namePart type="given">Chiara</namePart>
            <namePart type="family">Manna</namePart>
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        <name type="personal">
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        <name type="personal">
            <namePart type="given">Eva</namePart>
            <namePart type="family">Vanmassenhove</namePart>
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    <abstract>This study investigates gender bias in the Dutch RobBERT-2023 language model using an adapted version of the HONEST framework, which assesses harmful sentence completions. By translating and expanding HONEST templates to include non-binary and gender-neutral language, we systematically evaluate whether RobBERT-2023 exhibits biased or harmful outputs across gender identities. Our findings reveal that while the model’s overall bias score is relatively low, non-binary identities are disproportionately affected by derogatory language.</abstract>
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            <start>91</start>
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%0 Conference Proceedings
%T Evaluating Gender Bias in Dutch NLP: Insights from RobBERT-2023 and the HONEST Framework
%A Dewulf, Marie
%Y Hackenbuchner, Janiça
%Y Bentivogli, Luisa
%Y Daems, Joke
%Y Manna, Chiara
%Y Savoldi, Beatrice
%Y Vanmassenhove, Eva
%S Proceedings of the 3rd Workshop on Gender-Inclusive Translation Technologies (GITT 2025)
%D 2025
%8 June
%I European Association for Machine Translation
%C Geneva, Switzerland
%@ 978-2-9701897-4-9
%F dewulf-2025-evaluating
%X This study investigates gender bias in the Dutch RobBERT-2023 language model using an adapted version of the HONEST framework, which assesses harmful sentence completions. By translating and expanding HONEST templates to include non-binary and gender-neutral language, we systematically evaluate whether RobBERT-2023 exhibits biased or harmful outputs across gender identities. Our findings reveal that while the model’s overall bias score is relatively low, non-binary identities are disproportionately affected by derogatory language.
%U https://aclanthology.org/2025.gitt-1.7/
%P 91-92

Markdown (Informal)

[Evaluating Gender Bias in Dutch NLP: Insights from RobBERT-2023 and the HONEST Framework](https://aclanthology.org/2025.gitt-1.7/) (Dewulf, GITT 2025)

• Evaluating Gender Bias in Dutch NLP: Insights from RobBERT-2023 and the HONEST Framework (Dewulf, GITT 2025)

ACL

• Marie Dewulf. 2025. Evaluating Gender Bias in Dutch NLP: Insights from RobBERT-2023 and the HONEST Framework. In Proceedings of the 3rd Workshop on Gender-Inclusive Translation Technologies (GITT 2025), pages 91–92, Geneva, Switzerland. European Association for Machine Translation.