Large Language Models and Arabic Content: A Review

TL;DR

This paper reviews the application of large language models to Arabic NLP, discussing models, techniques, datasets, and challenges, highlighting recent progress and ongoing research efforts in this linguistically complex language.

Contribution

It provides a comprehensive overview of Arabic LLMs, including models, techniques, benchmarks, and challenges, emphasizing recent advancements and future directions.

Findings

Arabic LLMs achieve significant success in NLP tasks.

Fine-tuning and prompt engineering improve model performance.

Growing adoption of LLMs in Arabic NLP applications.

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.