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            <namePart type="given">Marta</namePart>
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    <abstract>Large Language Models (LLMs) offer promising alternatives to traditional occupational coding approaches in survey research. Using a German dataset, we examine the extent to which LLM-based occupational coding differs by gender. Our findings reveal systematic disparities: gendered job titles (e.g., “Autor” vs. “Autorin”, meaning “male author” vs. “female author”) frequently result in diverging occupation codes, even when semantically identical. Across all models, 54%–82% of gendered inputs obtain different Top-5 suggestions. The practical impact, however, depends on the model. GPT includes the correct code most often (62%) but demonstrates female bias (up to +18 pp). IBM is less accurate (51%) but largely balanced. Alibaba, Gemini, and MiniLM achieve about 50% correct-code inclusion, and their small (\ensuremath&lt; 10 pp) and direction-flipping gaps could indicate a sampling noise rather than gender bias. We discuss these findings in the context of fairness and reproducibility in NLP applications for social data.</abstract>
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%0 Conference Proceedings
%T Mind the Gap: Gender-based Differences in Occupational Embeddings
%A Kononykhina, Olga
%A Haensch, Anna-Carolina
%A Kreuter, Frauke
%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 kononykhina-etal-2025-mind
%X Large Language Models (LLMs) offer promising alternatives to traditional occupational coding approaches in survey research. Using a German dataset, we examine the extent to which LLM-based occupational coding differs by gender. Our findings reveal systematic disparities: gendered job titles (e.g., “Autor” vs. “Autorin”, meaning “male author” vs. “female author”) frequently result in diverging occupation codes, even when semantically identical. Across all models, 54%–82% of gendered inputs obtain different Top-5 suggestions. The practical impact, however, depends on the model. GPT includes the correct code most often (62%) but demonstrates female bias (up to +18 pp). IBM is less accurate (51%) but largely balanced. Alibaba, Gemini, and MiniLM achieve about 50% correct-code inclusion, and their small (\ensuremath< 10 pp) and direction-flipping gaps could indicate a sampling noise rather than gender bias. We discuss these findings in the context of fairness and reproducibility in NLP applications for social data.
%R 10.18653/v1/2025.gebnlp-1.7
%U https://aclanthology.org/2025.gebnlp-1.7/
%U https://doi.org/10.18653/v1/2025.gebnlp-1.7
%P 83-91

Markdown (Informal)

[Mind the Gap: Gender-based Differences in Occupational Embeddings](https://aclanthology.org/2025.gebnlp-1.7/) (Kononykhina et al., GeBNLP 2025)

• Mind the Gap: Gender-based Differences in Occupational Embeddings (Kononykhina et al., GeBNLP 2025)

ACL

• Olga Kononykhina, Anna-Carolina Haensch, and Frauke Kreuter. 2025. Mind the Gap: Gender-based Differences in Occupational Embeddings. In Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 83–91, Vienna, Austria. Association for Computational Linguistics.