most citedStereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models

13 citations · 13 across the 1 of their papers we have counts for

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1 paper

cs.CL2021★ 13 cited

Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models

Daniel de Vassimon Manela, David Errington, Thomas Fisher +2

This paper proposes two intuitive metrics, skew and stereotype, that quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun r…