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.

预训练语言模型存在各种社会偏见，本研究旨在对孟加拉语进行内在偏见测量，探讨现有偏见测量方法在孟加拉语中的必要改进，并研究上下文长度变化对偏见测量的影响，通过实验证明偏见度量指标对于上下文长度存在明显依赖性，在孟加拉语偏见分析中需要细致考虑。我们的工作为孟加拉语偏见测量提供了一个基础，我们公开提供所有资源以支持未来的研究。