StereoTales: Multilingual Open-Ended Stereotype Discovery in LLMs
type: NewsArticle
description: Researchers have developed StereoTales, a multilingual framework that uncovered over 1,500 harmful stereotypes by prompting 23 leading large language models to generate more than 650,000 open-ended stories across 10 languages. A human study involving 247 participants found that all models produced harmful associations, with stereotypes adapting culturally to the prompt language and amplifying bias against locally salient protected groups. The study also revealed systematic blind spots in how LLMs judge harm, with models underestimating harm on socio-economic attributes while overestimating it on gender.
date published: 2026-06-04 09:10:46+00:00
date modified: 2026-06-04 09:48:47.807027+00:00
language: en
is accessible for free: true
article body: # StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs We introduce StereoTales, a multilingual framework for discovering harmful stereotypes in open-ended LLM generation. By prompting 23 frontier models to write 650,000+ stories across 10 languages and statistically analyzing the demographic associations they produce, we surface over 1,500 significant stereotypes — many shared universally across all models. A human study with 247 participants provides a harmfulness classification of the associations. It shows that all LLMs generate harmful associations, and reveals systematic blind spots in how LLMs themselves judge harm. The harmful stereotypes are usually language-specific, or shared through cultural regions. Harmful associations adapt culturally to the prompt language and amplify bias against locally salient protected groups. ### Authors - Pierre Le Jeune, - Etienne Duchesne, - Stefano Palminteri, - Weixuan Xiao, - Bazire Houssin, - Benoît Malézieux, - Matteo Dora ### Published May 22, 2026 ## Introduction Well-known bias evaluation frameworks are saturated by recent LLMs. These frameworks mostly ask to recognize stereotypes or complete templated sentences. Yet, when given the freedom to generate open-ended stories, do these same frontier models fall back on harmful stereotypes? To answer this, we introduce **StereoTales**, a multilingual dataset and evaluation framework designed to uncover social biases in free-form text. By analyzing over 650,000 open-ended stories generated by 23 leading LLMs across 10 languages, we surface over 1,500 over-represented socio-demographic associations, which were subsequently evaluated for harmfulness by both a panel of human raters and the LLMs themselves. This article summarizes our [research preprint](https://arxiv.org/abs/2605.10442), which includes the full methodology, analyses, and limitations. Our method relies on [prompting models with a single demographic attribute](#open-ended-story-genera…
publisher: Web Pulse
keywords: large-language-models, ai-ethics, ai-safety, natural-language-processing, ai-research
mentions: Pierre Le Jeune
mentions: Etienne Duchesne
mentions: Stefano Palminteri
mentions: Weixuan Xiao
mentions: Bazire Houssin
mentions: Benoît Malézieux
mentions: Matteo Dora
mentions: StereoTales
citation: CreativeWork