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        <title>Evaluating and Mitigating Anti-LGBTQ Biases in German and Multilingual Language Models</title>
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    <abstract>While gender and racial biases in language models have been widely studied, anti-LGBTQ biases remain underexplored, particularly beyond English. Existing benchmarks often do not capture cultural and linguistic variation and rely on gender representations. This paper introduces a multilingual German-English benchmark dataset for the evaluation of anti-LGBTQ biases in language models. It combines community-sourced stereotypes from German-speaking queer individuals with a German translation of WinoQueer. The data is used to evaluate eight language models across sizes and architectures and explore mitigation through fine-tuning on community and progressive media content. Results show that language models reproduce anti-queer stereotypes, with variation across identities and models. Differences between the translated and community-based data highlight the importance of cultural adaptation for multilingual bias evaluation. Fine-tuning reduces bias on average, but not consistently across models and identities.</abstract>
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%0 Conference Proceedings
%T Evaluating and Mitigating Anti-LGBTQ Biases in German and Multilingual Language Models
%A Morch, Melina
%A Braun, Daniel
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F morch-braun-2026-evaluating
%X While gender and racial biases in language models have been widely studied, anti-LGBTQ biases remain underexplored, particularly beyond English. Existing benchmarks often do not capture cultural and linguistic variation and rely on gender representations. This paper introduces a multilingual German-English benchmark dataset for the evaluation of anti-LGBTQ biases in language models. It combines community-sourced stereotypes from German-speaking queer individuals with a German translation of WinoQueer. The data is used to evaluate eight language models across sizes and architectures and explore mitigation through fine-tuning on community and progressive media content. Results show that language models reproduce anti-queer stereotypes, with variation across identities and models. Differences between the translated and community-based data highlight the importance of cultural adaptation for multilingual bias evaluation. Fine-tuning reduces bias on average, but not consistently across models and identities.
%U https://aclanthology.org/2026.emnlp-main.2245/
%P 51072-51087

Markdown (Informal)

[Evaluating and Mitigating Anti-LGBTQ Biases in German and Multilingual Language Models](https://aclanthology.org/2026.emnlp-main.2245/) (Morch & Braun, EMNLP 2026)

• Evaluating and Mitigating Anti-LGBTQ Biases in German and Multilingual Language Models (Morch & Braun, EMNLP 2026)

ACL

• Melina Morch and Daniel Braun. 2026. Evaluating and Mitigating Anti-LGBTQ Biases in German and Multilingual Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 51072–51087, Budapest, Hungary. Association for Computational Linguistics.