From Guidelines to Practice: A New Paradigm for Arabic Language Model
  Evaluation
Authors: name: Serry Sibaee, name: Omer Nacar, name: Adel Ammar, name: Yasser Al-Habashi, name: Abdulrahman Al-Batati, name: Wadii Boulila

This paper addresses critical gaps in Arabic language model evaluation by
establishing comprehensive theoretical guidelines and introducing a novel
evaluation framework. We first analyze existing Arabic evaluation datasets,
identifying significant issues in linguistic accuracy, cultural alignment, and
methodological rigor. To address these limitations in LLMs, we present the
Arabic Depth Mini Dataset (ADMD), a carefully curated collection of 490
challenging questions spanning ten major domains (42 sub-domains, see Figure 1.
Using ADMD, we evaluate five leading language models: GPT-4, Claude 3.5 Sonnet,
Gemini Flash 1.5, CommandR 100B, and Qwen-Max. Our results reveal significant
variations in model performance across different domains, with particular
challenges in areas requiring deep cultural understanding and specialized
knowledge. Claude 3.5 Sonnet demonstrated the highest overall accuracy at 30\%,
showing relative strength in mathematical theory in Arabic, Arabic language,
and islamic domains. This work provides both theoretical foundations and
practical insights for improving Arabic language model evaluation, emphasizing
the importance of cultural competence alongside technical capabilities.