Papers by Victor Steinborn

1 papers
An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models (2022.findings-naacl)

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Challenge: Pretrained language models (PLMs) have been shown to encapsulate social biases, including those relating to gender and race.
Approach: They propose a new bias measure based on Jensen–Shannon divergence that retains more information from the model output probabilities than other previously proposed bias measures.
Outcome: The proposed measure outperforms CrowS-Pairs and other similar measures for non-English datasets.

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