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

Gender bias across LLMs is common and highly heterogeneous

Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with real consequences. Prior research has focused only on a small set of models, leaving open the extent to which gender biases are common and heterogeneous across LLMs. We a...

Cultural bias or universal traits? Exploring personality-like profiles of large language models

Do large language models (LLMs) exhibit coherent personality profiles, and are these profiles culturally neutral? This study used two validated personality inventories, the Big Five Inventory–2 and the Dark Triad Dirty Dozen, to assess five state-of-the-art LLMs (ChatGPT-4o, Gemini 2.0 Flash, DeepSeek-V3, Ernie 3.5 a...

Investigating How LLMs Propagate Female Stereotypes: Comparing What Models Say via Prompts with What They Represent in Their Embeddings

WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ Identities

While English language models have been widely examined for anti-queer bias, Dutch models remain understudied. To address this gap, we developed a culturally and linguistically adapted Dutch dataset based on the English WinoQueer benchmark, containing pairs of stereotypical and counter-stereotypical sentences. To valid...

How Humans and LLMs Read Gender into"Gender-Neutral"Physical Descriptions

The findings provide the first empirical evidence that seemingly"objective"physical descriptions can retain systematic gender associations in human interpretation, and uncover systematic patterns of model-human misalignment.

Harm Laundering in GPT Models: Evidence That Gender Discrimination Is Transformed Rather Than Reduced Across Safety-Trained Generations

Safety evaluations for large language models rely on surface-form classifiers that report declining harm scores across model generations. We provide evidence that this methodology is systematically incomplete: explicit discriminatory content is transformed rather than removed. We call this \emph{harm laundering}. Analy...