  While the impact of social biases in language models has been recognized,
prior methods for bias evaluation have been limited to binary association tests
on small datasets, limiting our understanding of bias complexities. This paper
proposes a novel framework for probing language models for social biases by
assessing disparate treatment, which involves treating individuals differently
according to their affiliation with a sensitive demographic group. We curate
SOFA, a large-scale benchmark designed to address the limitations of existing
fairness collections. SOFA expands the analysis beyond the binary comparison of
stereotypical versus anti-stereotypical identities to include a diverse range
of identities and stereotypes. Comparing our methodology with existing
benchmarks, we reveal that biases within language models are more nuanced than
acknowledged, indicating a broader scope of encoded biases than previously
recognized. Benchmarking LMs on SOFA, we expose how identities expressing
different religions lead to the most pronounced disparate treatments across all
models. Finally, our findings indicate that real-life adversities faced by
various groups such as women and people with disabilities are mirrored in the
behavior of these models.
