MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation

• URL: http://arxiv.org/abs/2503.10497v2
• Date: Mon, 26 May 2025 17:20:21 GMT
• Title: MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation
• Authors: 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, Irene Li,
• Abstract summary: MMLU-ProX is a benchmark covering 29 languages, built on an English benchmark.<n>Each language version consists of 11,829 identical questions, enabling direct cross-linguistic comparisons.<n>To meet efficient evaluation needs, we provide a lite version containing 658 questions per language.
• Score: 86.7047714187813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-linguistic reasoning abilities. This dual limitation makes it challenging to comprehensively assess LLMs' performance in the multilingual setting. 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-linguistic 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, with gaps of up to 24.3%. Through MMLU-ProX, we aim to advance the development of more inclusive AI systems and promote equitable access to technology across global contexts.

Related papers

• Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering [73.73820209993515]
  We introduce KoLasSimpleQA, the first benchmark evaluating the multilingual factual ability of Large Language Models (LLMs)<n>Inspired by existing research, we created the question set with features such as single knowledge point coverage, absolute objectivity, unique answers, and temporal stability.<n>Results show significant performance differences between the two domains.
  arXiv  Detail & Related papers  (2025-05-22T12:27:02Z)
• MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language [16.21019515431378]
  We propose MUG-Eval, a novel framework that evaluates large language models' multilingual generation capabilities.<n>We transform existing benchmarks into conversational tasks and measure the LLMs' accuracies on those tasks.<n>We evaluate 8 LLMs across 30 languages spanning high, mid, and low-resource categories, and we find that MUG-Eval correlates strongly with established benchmarks.
  arXiv  Detail & Related papers  (2025-05-20T14:14:00Z)
• PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts [79.84059473102778]
  PolyMath is a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels.<n>Our benchmark ensures difficulty comprehensiveness, language diversity, and high-quality translation.
  arXiv  Detail & Related papers  (2025-04-25T15:39:04Z)
• M-Prometheus: A Suite of Open Multilingual LLM Judges [64.22940792713713]
  We introduce M-Prometheus, a suite of open-weight LLM judges that can provide both direct assessment and pairwise comparison feedback on multilingual outputs. M-Prometheus models outperform state-of-the-art open LLM judges on multilingual reward benchmarks spanning more than 20 languages, as well as on literary machine translation (MT) evaluation covering 4 language pairs.
  arXiv  Detail & Related papers  (2025-04-07T11:37:26Z)
• GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models [11.714753007667941]
  GlotEval is a lightweight framework designed for massively multilingual evaluation. It supports seven key tasks (machine translation, text classification, summarization, open-ended generation, reading comprehension, sequence labeling, and intrinsic evaluation) spanning over dozens to hundreds of languages. It enables a precise diagnosis of model strengths and weaknesses in diverse linguistic contexts.
  arXiv  Detail & Related papers  (2025-04-05T12:30:58Z)
• TUMLU: A Unified and Native Language Understanding Benchmark for Turkic Languages [2.115206401188031]
  We propose two benchmarks for Turkic language MMLU: TUMLU and TUMLU-mini.<n>TUMLU-mini consists of middle- and high-school level questions spanning 11 academic subjects in Azerbaijani, Crimean Tatar, Karakalpak, Kazakh, Tatar, Turkish, Uyghur, and Uzbek.<n>We also present TUMLU-mini, a more concise, balanced, and manually verified subset of the dataset.
  arXiv  Detail & Related papers  (2025-02-16T07:07:38Z)
• All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages [73.93600813999306]
  ALM-bench is the largest and most comprehensive effort to date for evaluating LMMs across 100 languages.<n>It challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages.<n>The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions.
  arXiv  Detail & Related papers  (2024-11-25T15:44:42Z)
• Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following [51.18383180774354]
  We introduce Multi-IF, a new benchmark designed to assess Large Language Models' proficiency in following multi-turn and multilingual instructions. Our evaluation of 14 state-of-the-art LLMs on Multi-IF reveals that it presents a significantly more challenging task than existing benchmarks. languages with non-Latin scripts (Hindi, Russian, and Chinese) generally exhibit higher error rates, suggesting potential limitations in the models' multilingual capabilities.
  arXiv  Detail & Related papers  (2024-10-21T00:59:47Z)
• mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation [28.531581489405745]
  mHumanEval is an extended benchmark supporting prompts in over 200 natural languages.<n>We provide expert human translations for 15 diverse natural languages (NLs)<n>We conclude by analyzing the multilingual code generation capabilities of state-of-the-art (SOTA) Code LLMs.
  arXiv  Detail & Related papers  (2024-10-19T08:44:26Z)
• MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Models [0.5822010906632046]
  This study introduces MultiPragEval, the first pragmatic evaluation of Large Language Models (LLMs) Comprising 1200 question units categorized according to Grice's Cooperative Principle, MultiPragEval enables an in-depth assessment of LLMs' contextual awareness and their ability to infer implied meanings. Our findings demonstrate that Claude3-Opus significantly outperforms other models in all tested languages, establishing a state-of-the-art in the field.
  arXiv  Detail & Related papers  (2024-06-11T21:46:03Z)
• MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models [65.10456412127405]
  MLaKE is a benchmark for the adaptability of knowledge editing methods across five languages. MLaKE aggregates fact chains from Wikipedia across languages and generates questions in both free-form and multiple-choice. We evaluate the multilingual knowledge editing generalization capabilities of existing methods on MLaKE.
  arXiv  Detail & Related papers  (2024-04-07T15:23:28Z)
• OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models [59.54423478596468]
  We introduce OMGEval, the first Open-source Multilingual Generative test set that can assess the capability of LLMs in different languages. For each language, OMGEval provides 804 open-ended questions, covering a wide range of important capabilities of LLMs. Specifically, the current version of OMGEval includes 5 languages (i.e., Zh, Ru, Fr, Es, Ar)
  arXiv  Detail & Related papers  (2024-02-21T04:42:41Z)
• Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations [59.056367787688146]
  This paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. We construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages. By utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages.
  arXiv  Detail & Related papers  (2023-10-31T08:09:20Z)
• Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries [31.82249599013959]
  Large language models (LLMs) are transforming the ways the general public accesses and consumes information. LLMs demonstrate impressive language understanding and generation proficiencies, but concerns regarding their safety remain paramount. It remains unclear how these LLMs perform in the context of non-English languages.
  arXiv  Detail & Related papers  (2023-10-19T20:02:40Z)
• CMMLU: Measuring massive multitask language understanding in Chinese [133.70911295934746]
  This paper introduces a comprehensive Chinese benchmark that covers various subjects, including natural science, social sciences, engineering, and humanities. CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models within the Chinese context.
  arXiv  Detail & Related papers  (2023-06-15T15:49:51Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.