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From these, we apply statistical tests to identify more than 1{,}500 over-represented associations, which we then rate for harmfulness through both a panel of humans (N = 247) and the same LLMs. We report three main findings. \\textbf{(i)} Every model we evaluate emits consequential harmful stereotypes in open-ended generation, regardless of size or capabilities, and ","title":"StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs","url":"https://arxiv.org/abs/2605.10442","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.10442v2 Announce Type: cross \nAbstract: 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. 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