The Missing Minimal Pair: Stereotype Evaluation in LLMs
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Computer Science > Computation and Language
arXiv:2610.08747 (cs)
[Submitted on 6 Oct 2026]
Title:The Missing Minimal Pair: Stereotype Evaluation in LLMs
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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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.08747 [cs.CL] |
| (or arXiv:2610.08747v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08747
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Nataliya Stepanova [view email][v1] Tue, 6 Oct 2026 17:41:44 UTC (2,234 KB)
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View a PDF of the paper titled The Missing Minimal Pair: Stereotype Evaluation in LLMs, by Nataliya Stepanova and 3 other authors
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