  Large Language Models (LLMs) are trained on extensive web corpora, which
enable them to understand and generate human-like text. However, this training
process also results in inherent biases within the models. These biases arise
from web data's diverse and often uncurated nature, containing various
stereotypes and prejudices. Previous works on debiasing models rely on
benchmark datasets to measure their method's performance. However, these
datasets suffer from several pitfalls due to the highly subjective
understanding of bias, highlighting a critical need for contextual exploration.
We propose understanding the context of inputs by considering the diverse
situations in which they may arise. Our contribution is two-fold: (i) we
augment 2,291 stereotyped statements from two existing bias-benchmark datasets
with points for adding context; (ii) we develop the Context-Oriented Bias
Indicator and Assessment Score (COBIAS) to assess a statement's contextual
reliability in measuring bias. Our metric aligns with human judgment on
contextual reliability of statements (Spearman's $\rho = 0.65, p = 3.4 *
10^{-60}$) and can be used to create reliable datasets, which would assist bias
mitigation works.
