NAACL 2025long1 citations

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models

Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle, Manan Dey, Sil Hamilton

Abstract

Large Language Models (LLMs) reproduce and exacerbate the social biases present in their training data, and resources to quantify this issue are limited. While research has attempted to identify and mitigate such biases, most efforts have been concentrated around English, lagging the rapid advancement of LLMs in multilingual settings. In this paper, we introduce a new multilingual parallel dataset SHADES to help address this issue, designed for examining culturally-specific stereotypes that may be learned by LLMs. The dataset includes stereotypes from 20 regions around the world and 16 languages, spanning multiple identity categories subject to discrimination worldwide. We demonstrate its utility in a series of exploratory evaluations for both “base” and “instruction-tuned” language models. Our results suggest that stereotypes are consistently reflected across models and languages, with some languages and models indicating much stronger stereotype biases than others.

BibTeX
@inproceedings{mitchell-etal-2025-shades,
    title = "{SHADES}: Towards a Multilingual Assessment of Stereotypes in Large Language Models",
    author = "Mitchell, Margaret  and
      Attanasio, Giuseppe  and
      Baldini, Ioana  and
      Clinciu, Miruna  and
      Clive, Jordan  and
      Delobelle, Pieter  and
      Dey, Manan  and
      Hamilton, Sil  and
      Dill, Timm  and
      Doughman, Jad  and
      Dutt, Ritam  and
      Ghosh, Avijit  and
      Forde, Jessica Zosa  and
      Holtermann, Carolin  and
      Kaffee, Lucie-Aim{\'e}e  and
      Laud, Tanmay  and
      Lauscher, Anne  and
      Lopez-Davila, Roberto L  and
      Masoud, Maraim  and
      Nangia, Nikita  and
      Ovalle, Anaelia  and
      Pistilli, Giada  and
      Radev, Dragomir  and
      Savoldi, Beatrice  and
      Raheja, Vipul  and
      Qin, Jeremy  and
      Ploeger, Esther  and
      Subramonian, Arjun  and
      Dhole, Kaustubh  and
      Sun, Kaiser  and
      Djanibekov, Amirbek  and
      Mansurov, Jonibek  and
      Yin, Kayo  and
      Cueva, Emilio Villa  and
      Mukherjee, Sagnik  and
      Huang, Jerry  and
      Shen, Xudong  and
      Gala, Jay  and
      Al-Ali, Hamdan  and
      Tair Djanibekov  and
      Mukhituly, Nurdaulet  and
      Nie, Shangrui  and
      Sharma, Shanya  and
      Stanczak, Karolina  and
      Szczechla, Eliza  and
      Timponi Torrent, Tiago  and
      Tunuguntla, Deepak  and
      Viridiano, Marcelo  and
      Van Der Wal, Oskar  and
      Yakefu, Adina  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Zhang, Mike  and
      Zink, Sydney  and
      Talat, Zeerak",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.naacl-long.600/",
    pages = "11995--12041",
    ISBN = "979-8-89176-189-6"
}
SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models · NAACL 2025