From Body to Mind: Analyzing Gender Representation in Spanish Generative Language Models

Abstract

While Large Language Models (LLMs) demonstrate remarkable text generation capabilities, they also risk inheriting and perpetuating harmful societal biases present in their vast training data. This study presents a rigorous, large-scale analysis of gender bias in a diverse set of 20 publicly available Spanish generative LLMs, ranging from 760M to 11B parameters. Our methodology utilizes a comprehensive set of specifically designed sentence templates to elicit adjectival descriptions associated with men and women in neutral contexts. We then extract and manually classify these adjectives using the Supersenses lexicosemantic framework, focusing on four key domains: BODY, BEHAVIOR, FEELING, and MIND. Our research uncovers systematic patterns consistent with pervasive cultural stereotypes, echoing findings from earlier masked language models. Women are disproportionately described by physical and emotional attributes, whereas men are more frequently associated with behavioral and cognitive traits. Finally, we investigate the relationship between model size and the intensity of these observed gender biases, offering crucial insights into how scaling affects fairness and equity in non-English models.

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