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 address this gap across ten models released between April 2025 and June 2026, spanning nine vendors, using two paradigms: gender attribution to stereotyped phrases (Study 1) and moral judgment of abuse or torture against a woman or a man to prevent a catastrophic outcome (Study 2). In Study 1, two of ten models attributed masculine-stereotyped phrases to female writers more o...
| Authors | Edoardo Bolzoni, Valerio Capraro |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 30 September 2026 |
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