Large Language Models and Arabic Content: A Review

2025-05-12

AI Summary

Arabic NLP has long suffered from scarce resources, dialectal diversity, rich morphology, and pervasive orthographic variation. This paper presents the first systematic survey of large language models (LLMs) for Arabic processing, covering Arabic-specific pretraining, multidiaglect adaptation strategies, supervised/instruction fine-tuning, and prompt engineering techniques. It integrates major evaluation benchmarks—including ArabicMMLU and AQAD—to assess model capabilities across linguistic dimensions. The study elucidates how multilingual pretraining enhances morphological generalization and orthographic variant modeling in Arabic, identifies critical bottlenecks (e.g., inadequate low-resource dialect coverage and evaluation dataset bias), and pinpoints essential data gaps. Our analysis yields a technical roadmap for Arabic AI resource development, supporting the advancement of robust, multi-standard-compliant Arabic NLP systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Over the past three years, the rapid advancement of Large Language Models (LLMs) has had a profound impact on multiple areas of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) across diverse languages, including Arabic. Although Arabic is considered one of the most widely spoken languages across 27 countries in the Arabic world and used as a second language in some other non-Arabic countries as well, there is still a scarcity of Arabic resources, datasets, and tools. Arabic NLP tasks face various challenges due to the complexities of the Arabic language, including its rich morphology, intricate structure, and diverse writing standards, among other factors. Researchers have been actively addressing these challenges, demonstrating that pre-trained Large Language Models (LLMs) trained on multilingual corpora achieve significant success in various Arabic NLP tasks. This study provides an overview of using large language models (LLMs) for the Arabic language, highlighting early pre-trained Arabic Language models across various NLP applications and their ability to handle diverse Arabic content tasks and dialects. It also provides an overview of how techniques like finetuning and prompt engineering can enhance the performance of these models. Additionally, the study summarizes common Arabic benchmarks and datasets while presenting our observations on the persistent upward trend in the adoption of LLMs.

Problem

• Addressing scarcity of Arabic resources for LLMs
• Overcoming Arabic NLP challenges like complex morphology
• Enhancing LLM performance for Arabic dialects and tasks

Innovation

• Pre-trained LLMs for Arabic NLP tasks
• Fine-tuning and prompt engineering techniques
• Handling diverse Arabic dialects and content