ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
Fajri Koto Haonan Li Sara Shatnawi Jad Doughman Abdelrahman Boda Sadallah Aisha Alraeesi Khalid Almubarak Zaid Alyafeai Neha Sengupta Shady Shehata Nizar Habash Preslav Nakov Timothy Baldwin
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 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%.