  Language Models (LMs) have been shown to inherit undesired biases that might
hurt minorities and underrepresented groups if such systems were integrated
into real-world applications without careful fairness auditing. This paper
proposes FairBelief, an analytical approach to capture and assess beliefs,
i.e., propositions that an LM may embed with different degrees of confidence
and that covertly influence its predictions. With FairBelief, we leverage
prompting to study the behavior of several state-of-the-art LMs across
different previously neglected axes, such as model scale and likelihood,
assessing predictions on a fairness dataset specifically designed to quantify
LMs' outputs' hurtfulness. Finally, we conclude with an in-depth qualitative
assessment of the beliefs emitted by the models. We apply FairBelief to English
LMs, revealing that, although these architectures enable high performances on
diverse natural language processing tasks, they show hurtful beliefs about
specific genders. Interestingly, training procedure and dataset, model scale,
and architecture induce beliefs of different degrees of hurtfulness.
