SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models
An academic research paper introducing SHADES, a multilingual benchmark designed to evaluate how large language models (LLMs) generate and reinforce stereotypes across different languages and cultural contexts.
The paper presents SHADES (Stereotype Harms Across Diverse Evaluation Scenarios), a dataset and evaluation framework created to systematically measure stereotypical biases produced by large language models in multiple languages.
Researchers compile prompts and stereotype categories spanning different identities, social groups, and cultural contexts to test whether models generate harmful or biased associations. The study demonstrates that stereotype generation varies significantly across languages and models, highlighting the need for more inclusive evaluation benchmarks and mitigation strategies when developing globally deployed AI systems.
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