A rabic MMLU : Assessing Massive Multitask Language Understanding in A rabic (2024)

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%.

## introduction although large language models (llms) such as gpt-3.5 @xcite , bloomz @xcite , and jais @xcite have been pretrained with substantial coverage of modern standard arabic (msa), their reasoning and knowledge assessments are primarily conducted using datasets translated from english to arabic @xcite , which means there is limited capacity to evaluate content specific to arabic. this reliance on translation systems not only demonstrates an anglocentric approach @xcite but also potentially introduces errors and biases. given that arabic is one of the most widely-spoken languages in the world, with a speaker population of over 400 million people @xcite , it is critically important that datasets be constructed for the language that are regionally-and culturally-localized. the evaluation of language models has increasingly shifted from linguistically-centric tasks, such as part-of-speech (pos) tagging and named entity recognition (ner), towards reasoning and knowledge evaluation. this shift is evidenced in evaluations of models like @xcite , llama2 @xcite , and llm360 @xcite on various commonsense reasoning datasets @xcite @xcite , mathematical problems @xcite , coding challenges @xcite @xcite , and school exams @xcite @xcite . one notable dataset for knowledge evaluation is mmlu (massive multitask language understanding) @xcite , which comprises multiple-choice questions across various subjects based on the us education system. in recent arabic-centric llms like jais @xcite and acegpt @xcite , knowledge evaluation was carried out using mmlu translated from english to arabic. to comprehensively evaluate the reasoning and knowledge capabilities of arabic llms in local arabic-speaking contexts, we introduce arabicmmlu, styled around mmlu and sourced from school exams across arabic-speaking countries spanning north africa, the levant, and the gulf regions. arabicmmlu was constructed through collaboration with native arabic speakers from jordan, egypt, uae, lebanon, and saudi arabia (ksa), ensuring rich local context, particularly in the subject areas of history, geography, law, civics education, and driving tests. figure 1 summarizes the distribution of education levels and corresponding subjects in arabicmmlu. the proportion of primary school, middle school, high school, and university level questions in arabicmmlu are 22.2%, 12.2%, 34%, and 6.1%, respectively, with the remaining questions categorized as "na". our contributions can be summarized as follows: ## arabicmmlu in the middle east, the education system mostly follows the k12 system, consisting of six years of primary school, three years of middle school, and three years of high school. arabicmmlu is an arabic multiple-choice question-answering dataset comprising 40 tasks spanning a wide range of subjects and education levels. the questions are sourced from eight different countries in north africa (morocco and egypt), the levant (jordan, palestine, and lebanon), and the gulf (uae, kuwait, and ksa). each question has 2-5 candidate answers, with one correct answer. figures 2 and 3 showcase various examples of arabicmmlu questions, with some focusing on history, driving tests, social science, and bar exams, all of which are pertinent to arabic-specific norms and cultures. notably, arabic multiple-choice questions sometimes use arabic-script characters ( , , , , ) rather than latin-script characters (e.g. a, b, c, d, e). this differs from many other languages, where the answer options are strictly in latin script (even if the local writing script is not latin, as with mandarin chinese). in prior work @xcite @xcite , answering these multiple-choice questions has relied on the probability of the alphabetic options. we experiment with both arabic and latin script outputs in section 4. ## conclusion and future work we introduce arabicmmlu, the first large-scale multi-task language understanding dataset designed to evaluate real-world knowledge in arabic. through experiments with over 14k multiplechoice questions spanning various subjects and education levels, we observed that arabic-centric llms outperform multilingual llms, albeit with lower accuracy than gpt-4. we envision arabicmmlu as a valuable resource for tracking the real-world knowledge and reasoning capabilities of future arabic llms. for future work, arabicmmlu can be extended to include short-answer or essay questions, different modalities (i.e., images, audio, video), larger region coverage, and more questions in professional settings. this will enhance the evaluation to better reflect real-world scenarios.