Language models show higher offensive stereotyping bias against overlooked marginalized groups in English and German, while Arabic models show high bias against both marginalized and dominant groups, especially for religion and ethnicity.
QueerBench: Quantifying Discrimination in Language Models Toward Queer Identities
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abstract
With the increasing role of Natural Language Processing (NLP) in various applications, challenges concerning bias and stereotype perpetuation are accentuated, which often leads to hate speech and harm. Despite existing studies on sexism and misogyny, issues like homophobia and transphobia remain underexplored and often adopt binary perspectives, putting the safety of LGBTQIA+ individuals at high risk in online spaces. In this paper, we assess the potential harm caused by sentence completions generated by English large language models (LLMs) concerning LGBTQIA+ individuals. This is achieved using QueerBench, our new assessment framework, which employs a template-based approach and a Masked Language Modeling (MLM) task. The analysis indicates that large language models tend to exhibit discriminatory behaviour more frequently towards individuals within the LGBTQIA+ community, reaching a difference gap of 7.2% in the QueerBench score of harmfulness.
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Out of Sight Out of Mind, Out of Sight Out of Mind: Measuring Bias in Language Models Against Overlooked Marginalized Groups in Regional Contexts
Language models show higher offensive stereotyping bias against overlooked marginalized groups in English and German, while Arabic models show high bias against both marginalized and dominant groups, especially for religion and ethnicity.