The Missing Minimal Pair: Stereotype Evaluation in LLMs

Abstract:A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences. We argue that such single-pair comparisons are often unreliable: simply rewriting the same stereotype with an alternative attribute can yield logically inconsistent preferences. To address this, we propose a dual minimal pair setup that introduces two axes of comparison for robust stereotype evaluation. First, we present a data-augmentation framework that fills critical gaps in existing stereotype datasets by generating paraphrases and alternate attributes. We apply our framework on a set of English, Russian, Spanish and Chinese stereotypes. Second, we introduce two evaluation metrics tailored to the dual minimal pair setup. One of these metrics provides a new perspective on bias by modeling the mutual information (MI) between social groups and stereotyped attributes. This MI-based metric is better suited for aggregation and enables more robust comparisons of stereotype strength across different languages and models. Our code is available at this https URL.

Computation and Language (cs.CL)

arXiv:2610.08747 [cs.CL]

(or arXiv:2610.08747v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.08747 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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