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New Dutch dataset WinoQueer-NL reveals significant bias against LGBTQ+ identities in language models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed WinoQueer-NL, a new dataset designed to assess bias in Dutch language models concerning LGBTQ+ identities. This dataset, adapted from the English WinoQueer benchmark and validated through a survey with 43 Dutch queer participants, contains over 42,000 sentences. Initial evaluations using various Dutch and multilingual models revealed that while the average bias score was neutral, specific models exhibited significant bias, particularly against transgender and non-binary individuals, favoring stereotypical sentences up to 97% of the time for transgender identities. AI

IMPACT Highlights the need for culturally specific datasets to identify and mitigate biases in AI models, particularly impacting marginalized communities.

RANK_REASON The cluster describes a new academic paper introducing a dataset for evaluating bias in language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Dutch dataset WinoQueer-NL reveals significant bias against LGBTQ+ identities in language models

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The cluster describes a new academic paper introducing a dataset for evaluating bias in language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ Identities

    While English language models have been widely examined for anti-queer bias, Dutch models remain understudied. To address this gap, we developed a culturally and linguistically adapted Dutch dataset based on the English WinoQueer benchmark, containing pairs of stereotypical and c…

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