@inproceedings{gnadt-etal-2025-exploring,
title = "Exploring Gender Bias in Large Language Models: An In-depth Dive into the {G}erman Language",
author = {Gnadt, Kristin and
Thulke, David and
Kopeinik, Simone and
Schl{\"u}ter, Ralf},
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Sta{\'n}czak, Karolina and
Nozza, Debora",
booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.gebnlp-1.37/",
doi = "10.18653/v1/2025.gebnlp-1.37",
pages = "427--450",
ISBN = "979-8-89176-277-0",
abstract = "In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods initially developed for the English language when applied to other languages. This work aims to contribute to this research strand by presenting five German datasets for gender bias evaluation in LLMs. The datasets are grounded in well-established concepts of gender bias and are accessible through multiple methodologies. Our findings, reported for eight multilingual LLM models, reveal unique challenges associated with gender bias in German, including the ambiguous interpretation of male occupational terms and the influence of seemingly neutral nouns on gender perception. This work contributes to the understanding of gender bias in LLMs across languages and underscores the necessity for tailored evaluation frameworks."
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<abstract>In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods initially developed for the English language when applied to other languages. This work aims to contribute to this research strand by presenting five German datasets for gender bias evaluation in LLMs. The datasets are grounded in well-established concepts of gender bias and are accessible through multiple methodologies. Our findings, reported for eight multilingual LLM models, reveal unique challenges associated with gender bias in German, including the ambiguous interpretation of male occupational terms and the influence of seemingly neutral nouns on gender perception. This work contributes to the understanding of gender bias in LLMs across languages and underscores the necessity for tailored evaluation frameworks.</abstract>
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%0 Conference Proceedings
%T Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language
%A Gnadt, Kristin
%A Thulke, David
%A Kopeinik, Simone
%A Schlüter, Ralf
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Stańczak, Karolina
%Y Nozza, Debora
%S Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-277-0
%F gnadt-etal-2025-exploring
%X In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods initially developed for the English language when applied to other languages. This work aims to contribute to this research strand by presenting five German datasets for gender bias evaluation in LLMs. The datasets are grounded in well-established concepts of gender bias and are accessible through multiple methodologies. Our findings, reported for eight multilingual LLM models, reveal unique challenges associated with gender bias in German, including the ambiguous interpretation of male occupational terms and the influence of seemingly neutral nouns on gender perception. This work contributes to the understanding of gender bias in LLMs across languages and underscores the necessity for tailored evaluation frameworks.
%R 10.18653/v1/2025.gebnlp-1.37
%U https://aclanthology.org/2025.gebnlp-1.37/
%U https://doi.org/10.18653/v1/2025.gebnlp-1.37
%P 427-450
Markdown (Informal)
[Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language](https://aclanthology.org/2025.gebnlp-1.37/) (Gnadt et al., GeBNLP 2025)
ACL