# Large Language Models and Arabic Content: A Review

arXiv: https://arxiv.org/abs/2505.08004 · HTML: https://mlsanity.com/paper/2505.08004/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 18 of 18)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [Pre-Training BERT on Arabic Tweets: Practical Considerations](https://mlsanity.com/paper/2102.10684/) (2021)
- [An Empirical Evaluation of LLMs for Solving Offensive Security Challenges](https://mlsanity.com/paper/2402.11814/) (2024)
- [DziriBERT: a Pre-trained Language Model for the Algerian Dialect](https://mlsanity.com/paper/2109.12346/) (2021)
- [TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties](https://mlsanity.com/paper/2308.03051/) (2023)
- [Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models](https://mlsanity.com/paper/2306.16322/) (2023)
- [AraPoemBERT: A Pretrained Language Model for Arabic Poetry Analysis](https://mlsanity.com/paper/2403.12392/) (2024)
- [AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing](https://mlsanity.com/paper/2306.06800/) (2023)
- [ArabianGPT: Native Arabic GPT-based Large Language Model](https://mlsanity.com/paper/2402.15313/) (2024)
- [ChatGPT for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2308.04492/) (2023)
- [AraSpell: A Deep Learning Approach for Arabic Spelling Correction](https://mlsanity.com/paper/2405.06981/) (2024)
- [EgyBERT: A Large Language Model Pretrained on Egyptian Dialect Corpora](https://mlsanity.com/paper/2408.03524/) (2024)
- [Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning](https://mlsanity.com/paper/2108.01141/) (2021)

## Built on by (most cited first, 4 of 4)

- [Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps](https://mlsanity.com/paper/2510.13430/) (2025)
- [AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data](https://mlsanity.com/paper/2507.18442/) (2025)
- [MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation](https://mlsanity.com/paper/2604.18914/) (2026)
- [A Comprehensive Analysis of Arabic Natural Language Processing Research: Trends, Topic Evolution, and Research Gaps -- A Bibliometric and Topic-Based Study](https://mlsanity.com/paper/2608.23421/) (2026)

## Abstract (arXiv)

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.