Work

Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language

Year: 2025

Type: conference-paper

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.

Source: Workshop on Gender Bias in Natural Language Processing (GeBNLP)

DOI: 10.18653/v1/2025.gebnlp-1.37

Authors: Kristin Gnadt, David Thulke, Simone Kopeinik, Ralf Schlüter

Institutions: RWTH Aachen University, Know Center Research GmbH (Austria)

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Gender equality

Open Access status: hybrid

Funder: RWTH Aachen University