@inproceedings{koto-etal-2024-arabicmmlu,
title = "{A}rabic{MMLU}: Assessing Massive Multitask Language Understanding in {A}rabic",
author = "Koto, Fajri and
Li, Haonan and
Shatnawi, Sara and
Doughman, Jad and
Sadallah, Abdelrahman and
Alraeesi, Aisha and
Almubarak, Khalid and
Alyafeai, Zaid and
Sengupta, Neha and
Shehata, Shady and
Habash, Nizar and
Nakov, Preslav and
Baldwin, Timothy",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.334",
pages = "5622--5640",
abstract = "The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for the Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLama2, and Falcon struggle to achieve a score of 50{\%}, while even the top-performing Arabic-centric model only achieves a score of 62.3{\%}.",
}
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%0 Conference Proceedings
%T ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
%A Koto, Fajri
%A Li, Haonan
%A Shatnawi, Sara
%A Doughman, Jad
%A Sadallah, Abdelrahman
%A Alraeesi, Aisha
%A Almubarak, Khalid
%A Alyafeai, Zaid
%A Sengupta, Neha
%A Shehata, Shady
%A Habash, Nizar
%A Nakov, Preslav
%A Baldwin, Timothy
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F koto-etal-2024-arabicmmlu
%X The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for the Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLama2, and Falcon struggle to achieve a score of 50%, while even the top-performing Arabic-centric model only achieves a score of 62.3%.
%U https://aclanthology.org/2024.findings-acl.334
%P 5622-5640
Markdown (Informal)
[ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://aclanthology.org/2024.findings-acl.334) (Koto et al., Findings 2024)
ACL
- Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman, Abdelrahman Sadallah, Aisha Alraeesi, Khalid Almubarak, Zaid Alyafeai, Neha Sengupta, Shady Shehata, Nizar Habash, Preslav Nakov, and Timothy Baldwin. 2024. ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic. In Findings of the Association for Computational Linguistics ACL 2024, pages 5622–5640, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.