{
  "id": 4558,
  "url": "https://arxiv.org/abs/2605.10442v2",
  "title": "StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs",
  "summary": "Multilingual studies of social bias in open-ended LLM generation remain limited: most existing benchmarks are English-centric, template-based, or restricted to recognizing pre-specified stereotypes. We introduce StereoTales, a multilingual dataset and evaluation pipeline for systematically studying the emergence of social bias in open-ended LLM generation. The dataset covers 10 languages and 79 socio-demographic attributes, and comprises over 650k stories generated by 23 recent LLMs, each annota",
  "authors": "Pierre Le Jeune, Étienne Duchesne, Weixuan Xiao, Stefano Palminteri, Bazire Houssin, Benoît Malézieux et al.",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T12:12:28.000Z",
  "fetched_at": "2026-07-14T16:31:03.581Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4558",
  "original_url": "https://arxiv.org/abs/2605.10442v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}