@inproceedings{singh-etal-2025-global,
title = "Global {MMLU}: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation",
author = "Singh, Shivalika and
Romanou, Angelika and
Fourrier, Cl{\'e}mentine and
Adelani, David Ifeoluwa and
Ngui, Jian Gang and
Vila-Suero, Daniel and
Limkonchotiwat, Peerat and
Marchisio, Kelly and
Leong, Wei Qi and
Susanto, Yosephine and
Ng, Raymond and
Longpre, Shayne and
Ruder, Sebastian and
Ko, Wei-Yin and
Bosselut, Antoine and
Oh, Alice and
Martins, Andre and
Choshen, Leshem and
Ippolito, Daphne and
Ferrante, Enzo and
Fadaee, Marzieh and
Ermis, Beyza and
Hooker, Sara",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.919/",
doi = "10.18653/v1/2025.acl-long.919",
pages = "18761--18799",
ISBN = "979-8-89176-251-0",
abstract = "Reliable multilingual evaluation is difficult, and culturally appropriate evaluation is even harder to achieve.A common practice to fill this gap is to machine-translate English evaluation sets. However, translation introduces language bias and carries over cultural and regional assumptions from the original questions {--} often testing knowledge irrelevant to the target audience. In this work, we highlight the extent and impact of these biases and present a multilingual evaluation framework that aims to mitigate them through improved translations and annotation practices.Through a large-scale study involving professional and community translators and annotators, we show that state-of-the-art models excel primarily by learning Western-centric concepts. Notably, we find that model rankings on the full MMLU change when evaluated on a subset of questions explicitly marked as culturally sensitive.We release Global MMLU, a multilingual extension of MMLU across 42 languages, featuring improved translation quality, expanded language coverage, and designated subsets labeled as culturally sensitive and culturally agnostic to enable a more comprehensive and equitable benchmark for evaluating language models across diverse linguistic and cultural contexts."
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%0 Conference Proceedings
%T Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation
%A Singh, Shivalika
%A Romanou, Angelika
%A Fourrier, Clémentine
%A Adelani, David Ifeoluwa
%A Ngui, Jian Gang
%A Vila-Suero, Daniel
%A Limkonchotiwat, Peerat
%A Marchisio, Kelly
%A Leong, Wei Qi
%A Susanto, Yosephine
%A Ng, Raymond
%A Longpre, Shayne
%A Ruder, Sebastian
%A Ko, Wei-Yin
%A Bosselut, Antoine
%A Oh, Alice
%A Martins, Andre
%A Choshen, Leshem
%A Ippolito, Daphne
%A Ferrante, Enzo
%A Fadaee, Marzieh
%A Ermis, Beyza
%A Hooker, Sara
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F singh-etal-2025-global
%X Reliable multilingual evaluation is difficult, and culturally appropriate evaluation is even harder to achieve.A common practice to fill this gap is to machine-translate English evaluation sets. However, translation introduces language bias and carries over cultural and regional assumptions from the original questions – often testing knowledge irrelevant to the target audience. In this work, we highlight the extent and impact of these biases and present a multilingual evaluation framework that aims to mitigate them through improved translations and annotation practices.Through a large-scale study involving professional and community translators and annotators, we show that state-of-the-art models excel primarily by learning Western-centric concepts. Notably, we find that model rankings on the full MMLU change when evaluated on a subset of questions explicitly marked as culturally sensitive.We release Global MMLU, a multilingual extension of MMLU across 42 languages, featuring improved translation quality, expanded language coverage, and designated subsets labeled as culturally sensitive and culturally agnostic to enable a more comprehensive and equitable benchmark for evaluating language models across diverse linguistic and cultural contexts.
%R 10.18653/v1/2025.acl-long.919
%U https://aclanthology.org/2025.acl-long.919/
%U https://doi.org/10.18653/v1/2025.acl-long.919
%P 18761-18799
Markdown (Informal)
[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://aclanthology.org/2025.acl-long.919/) (Singh et al., ACL 2025)
ACL
- Shivalika Singh, Angelika Romanou, Clémentine Fourrier, David Ifeoluwa Adelani, Jian Gang Ngui, Daniel Vila-Suero, Peerat Limkonchotiwat, Kelly Marchisio, Wei Qi Leong, Yosephine Susanto, Raymond Ng, Shayne Longpre, Sebastian Ruder, Wei-Yin Ko, Antoine Bosselut, Alice Oh, Andre Martins, Leshem Choshen, Daphne Ippolito, Enzo Ferrante, Marzieh Fadaee, Beyza Ermis, and Sara Hooker. 2025. Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18761–18799, Vienna, Austria. Association for Computational Linguistics.