Daily Papers

Pretrained language models inherently exhibit various social biases, prompting a crucial examination of their social impact across various linguistic contexts due to their widespread usage. Previous studies have provided numerous methods for intrinsic bias measurements, predominantly focused on high-resource languages. In this work, we aim to extend these investigations to Bangla, a low-resource language. Specifically, in this study, we (1) create a dataset for intrinsic gender bias measurement in Bangla, (2) discuss necessary adaptations to apply existing bias measurement methods for Bangla, and (3) examine the impact of context length variation on bias measurement, a factor that has been overlooked in previous studies. Through our experiments, we demonstrate a clear dependency of bias metrics on context length, highlighting the need for nuanced considerations in Bangla bias analysis. We consider our work as a stepping stone for bias measurement in the Bangla Language and make all of our resources publicly available to support future research.

The multifaceted challenge of accurately measuring gender stereotypical bias in language models is akin to discerning different segments of a broader, unseen entity. This short paper primarily focuses on intrinsic bias mitigation and measurement strategies for language models, building on prior research that demonstrates a lack of correlation between intrinsic and extrinsic approaches. We delve deeper into intrinsic measurements, identifying inconsistencies and suggesting that these benchmarks may reflect different facets of gender stereotype. Our methodology involves analyzing data distributions across datasets and integrating gender stereotype components informed by social psychology. By adjusting the distribution of two datasets, we achieve a better alignment of outcomes. Our findings underscore the complexity of gender stereotyping in language models and point to new directions for developing more refined techniques to detect and reduce bias.