Large language models have been shown to encode a variety of social biases,
which carries the risk of downstream harms. While the impact of these biases
has been recognized, prior methods for bias evaluation have been limited to
binary association tests on small datasets, offering a constrained view of the
nature of societal biases within language models. In this paper, we propose an
original framework for probing language models for societal biases. We collect
a probing dataset to analyze language models' general associations, as well as
along the axes of societal categories, identities, and stereotypes. To this
end, we leverage a novel perplexity-based fairness score. We curate a
large-scale benchmarking dataset addressing drawbacks and limitations of
existing fairness collections, expanding to a variety of different identities
and stereotypes. When comparing our methodology with prior work, we demonstrate
that biases within language models are more nuanced than previously
acknowledged. In agreement with recent findings, we find that larger model
variants exhibit a higher degree of bias. Moreover, we expose how identities
expressing different religions lead to the most pronounced disparate treatments
across all models.

本研究提出了一种新的框架，用于探索语言模型中的社会偏见，通过采集探索数据集和利用一种新的公平性评分方法，发现语言模型中的偏见更加复杂，并揭示不同宗教身份导致各种模型中最明显的不平等处理。