@inproceedings{xuan-etal-2025-mmlu,
title = "{MMLU}-{P}ro{X}: A Multilingual Benchmark for Advanced Large Language Model Evaluation",
author = "Xuan, Weihao and
Yang, Rui and
Qi, Heli and
Zeng, Qingcheng and
Xiao, Yunze and
Feng, Aosong and
Liu, Dairui and
Xing, Yun and
Wang, Junjue and
Gao, Fan and
Lu, Jinghui and
Jiang, Yuang and
Li, Huitao and
Li, Xin and
Yu, Kunyu and
Dong, Ruihai and
Gu, Shangding and
Li, Yuekang and
Xie, Xiaofei and
Juefei-Xu, Felix and
Khomh, Foutse and
Yoshie, Osamu and
Chen, Qingyu and
Teodoro, Douglas and
Liu, Nan and
Goebel, Randy and
Ma, Lei and
Marrese-Taylor, Edison and
Lu, Shijian and
Iwasawa, Yusuke and
Matsuo, Yutaka and
Li, Irene",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.79/",
pages = "1513--1532",
ISBN = "979-8-89176-332-6",
abstract = "Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs' performance in the multilingual setting comprehensively. To fill this gap, we introduce MMLU-ProX, a comprehensive benchmark covering 29 languages, built on an English benchmark. Each language version consists of 11,829 identical questions, enabling direct cross-lingual comparisons. Additionally, to meet efficient evaluation needs, we provide a lite version containing 658 questions per language. To ensure the high quality of MMLU-ProX, we employ a rigorous development process that involves multiple powerful LLMs for translation, followed by expert review to ensure accurate expression, consistent terminology, and cultural relevance. Building on this, we systematically evaluate 36 state-of-the-art LLMs, including reasoning-enhanced and multilingual-optimized LLMs. The results reveal significant disparities in the multilingual capabilities of LLMs: While they perform well in high-resource languages, their performance declines markedly in low-resource languages, particularly for African languages. Through MMLU-ProX, we aim to advance the development of more inclusive AI systems and promote equitable access to technology across global contexts."
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<abstract>Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs’ performance in the multilingual setting comprehensively. To fill this gap, we introduce MMLU-ProX, a comprehensive benchmark covering 29 languages, built on an English benchmark. Each language version consists of 11,829 identical questions, enabling direct cross-lingual comparisons. Additionally, to meet efficient evaluation needs, we provide a lite version containing 658 questions per language. To ensure the high quality of MMLU-ProX, we employ a rigorous development process that involves multiple powerful LLMs for translation, followed by expert review to ensure accurate expression, consistent terminology, and cultural relevance. Building on this, we systematically evaluate 36 state-of-the-art LLMs, including reasoning-enhanced and multilingual-optimized LLMs. The results reveal significant disparities in the multilingual capabilities of LLMs: While they perform well in high-resource languages, their performance declines markedly in low-resource languages, particularly for African languages. Through MMLU-ProX, we aim to advance the development of more inclusive AI systems and promote equitable access to technology across global contexts.</abstract>
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%0 Conference Proceedings
%T MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation
%A Xuan, Weihao
%A Yang, Rui
%A Qi, Heli
%A Zeng, Qingcheng
%A Xiao, Yunze
%A Feng, Aosong
%A Liu, Dairui
%A Xing, Yun
%A Wang, Junjue
%A Gao, Fan
%A Lu, Jinghui
%A Jiang, Yuang
%A Li, Huitao
%A Li, Xin
%A Yu, Kunyu
%A Dong, Ruihai
%A Gu, Shangding
%A Li, Yuekang
%A Xie, Xiaofei
%A Juefei-Xu, Felix
%A Khomh, Foutse
%A Yoshie, Osamu
%A Chen, Qingyu
%A Teodoro, Douglas
%A Liu, Nan
%A Goebel, Randy
%A Ma, Lei
%A Marrese-Taylor, Edison
%A Lu, Shijian
%A Iwasawa, Yusuke
%A Matsuo, Yutaka
%A Li, Irene
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F xuan-etal-2025-mmlu
%X Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs’ performance in the multilingual setting comprehensively. To fill this gap, we introduce MMLU-ProX, a comprehensive benchmark covering 29 languages, built on an English benchmark. Each language version consists of 11,829 identical questions, enabling direct cross-lingual comparisons. Additionally, to meet efficient evaluation needs, we provide a lite version containing 658 questions per language. To ensure the high quality of MMLU-ProX, we employ a rigorous development process that involves multiple powerful LLMs for translation, followed by expert review to ensure accurate expression, consistent terminology, and cultural relevance. Building on this, we systematically evaluate 36 state-of-the-art LLMs, including reasoning-enhanced and multilingual-optimized LLMs. The results reveal significant disparities in the multilingual capabilities of LLMs: While they perform well in high-resource languages, their performance declines markedly in low-resource languages, particularly for African languages. Through MMLU-ProX, we aim to advance the development of more inclusive AI systems and promote equitable access to technology across global contexts.
%U https://aclanthology.org/2025.emnlp-main.79/
%P 1513-1532
Markdown (Informal)
[MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation](https://aclanthology.org/2025.emnlp-main.79/) (Xuan et al., EMNLP 2025)
ACL
- Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, and Irene Li. 2025. MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 1513–1532, Suzhou, China. Association for Computational Linguistics.