  Gender bias in text corpora used in various natural language processing (NLP)
contexts, such as for training large language models (LLMs), can lead to the
perpetuation and amplification of societal inequalities. This is particularly
pronounced in gendered languages like Spanish or French, where grammatical
structures inherently encode gender, making the bias analysis more challenging.
Existing methods designed for English are inadequate for this task due to the
intrinsic linguistic differences between English and gendered languages. This
paper introduces a novel methodology that leverages the contextual
understanding capabilities of LLMs to quantitatively analyze gender
representation in Spanish corpora. By utilizing LLMs to identify and classify
gendered nouns and pronouns in relation to their reference to human entities,
our approach provides a nuanced analysis of gender biases. We empirically
validate our method on four widely-used benchmark datasets, uncovering
significant gender disparities with a male-to-female ratio ranging from 4:1 to
6:1. These findings demonstrate the value of our methodology for bias
quantification in gendered languages and suggest its application in NLP,
contributing to the development of more equitable language technologies.
