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Numerous types of social biases have been identified in pre-trained languagemodels (PLMs), and various intrinsic bias evaluation measures have beenproposed for quantifying those social biases. Prior works have relied on humanannotated examples to compare existing intrinsic bias evaluation measures.However, this approach is not easily adaptable to different languages noramenable to large scale evaluations due to the costs and difficulties whenrecruiting human annotators. To overcome this limitation, we propose a methodto compare intrinsic gender bias evaluation measures without relying onhuman-annotated examples. Specifically, we create multiple bias-controlledversions of PLMs using varying amounts of male vs. female gendered sentences,mined automatically from an unannotated corpus using gender-related word lists.Next, each bias-controlled PLM is evaluated using an intrinsic bias evaluationmeasure, and the rank correlation between the computed bias scores and thegender proportions used to fine-tune the PLMs is computed. Experiments onmultiple corpora and PLMs repeatedly show that the correlations reported by ourproposed method that does not require human annotated examples are comparableto those computed using human annotated examples in prior work.

Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples