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Measuring Gender Bias in German Language Generation

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2022

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Gesellschaft für Informatik, Bonn

Zusammenfassung

Most existing methods to measure social bias in natural language generation are specified for English language models. In this work, we developed a German regard classifier based on a newly crowd-sourced dataset. Our model meets the test set accuracy of the original English version. With the classifier, we measured binary gender bias in two large language models. The results indicate a positive bias toward female subjects for a German version of GPT-2 and similar tendencies for GPT-3. Yet, upon qualitative analysis, we found that positive regard partly corresponds to sexist stereotypes. Our findings suggest that the regard classifier should not be used as a single measure but, instead, combined with more qualitative analyses.

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Kraft,Angelie; Zorn,Hans-Peter; Fecht,Pascal; Simon,Judith; Biemann,Chris; Usbeck,Ricardo (2022): Measuring Gender Bias in German Language Generation. INFORMATIK 2022. DOI: 10.18420/inf2022_108. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-720-3. pp. 1257-1274. Trustworthy AI in Science and Society. Hamburg. 26.-30. September 2022

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