# The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology

arXiv: https://arxiv.org/abs/2506.01340 · HTML: https://mlsanity.com/paper/2506.01340/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [EuroLLM: Multilingual Language Models for Europe](https://mlsanity.com/paper/2409.16235/) (2024)
- [SeaLLMs -- Large Language Models for Southeast Asia](https://mlsanity.com/paper/2312.00738/) (2023)
- [AceGPT, Localizing Large Language Models in Arabic](https://mlsanity.com/paper/2309.12053/) (2023)
- [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://mlsanity.com/paper/2308.16149/) (2023)
- [Fanar: An Arabic-Centric Multimodal Generative AI Platform](https://mlsanity.com/paper/2501.13944/) (2025)
- [AraT5: Text-to-Text Transformers for Arabic Language Generation](https://mlsanity.com/paper/2109.12068/) (2021)
- [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://mlsanity.com/paper/2212.10758/) (2022)
- [Pre-Training BERT on Arabic Tweets: Practical Considerations](https://mlsanity.com/paper/2102.10684/) (2021)
- [Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks](https://mlsanity.com/paper/2403.01031/) (2024)
- [Dolphin: A Challenging and Diverse Benchmark for Arabic NLG](https://mlsanity.com/paper/2305.14989/) (2023)
- [CIDAR: Culturally Relevant Instruction Dataset For Arabic](https://mlsanity.com/paper/2402.03177/) (2024)
- [CAMEL-Bench: A Comprehensive Arabic LMM Benchmark](https://mlsanity.com/paper/2410.18976/) (2024)
- [Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic](https://mlsanity.com/paper/2407.18129/) (2024)
- [A Survey of Large Language Models for Arabic Language and its Dialects](https://mlsanity.com/paper/2410.20238/) (2024)
- [JASMINE: Arabic GPT Models for Few-Shot Learning](https://mlsanity.com/paper/2212.10755/) (2022)
- [ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models](https://mlsanity.com/paper/2408.07983/) (2024)
- [MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language](https://mlsanity.com/paper/2409.12257/) (2024)
- [The Qiyas Benchmark: Measuring ChatGPT Mathematical and Language Understanding in Arabic](https://mlsanity.com/paper/2407.00146/) (2024)
- [Language and Planning in Robotic Navigation: A Multilingual Evaluation of State-of-the-Art Models](https://mlsanity.com/paper/2501.05478/) (2025)

## Built on by (most cited first, 12 of 12)

- [Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps](https://mlsanity.com/paper/2510.13430/) (2025)
- [From RAG to Agentic RAG for Faithful Islamic Question Answering](https://mlsanity.com/paper/2601.07528/) (2026)
- [Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale](https://mlsanity.com/paper/2509.14008/) (2025)
- [MENASpeechBank: A Reference Voice Bank with Persona-Conditioned Multi-Turn Conversations for AudioLLMs](https://mlsanity.com/paper/2602.07036/) (2026)
- [AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models](https://mlsanity.com/paper/2511.14295/) (2025)
- [Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models](https://mlsanity.com/paper/2602.05437/) (2026)
- [Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith](https://mlsanity.com/paper/2603.23972/) (2026)
- [Aslema at NADI 2026: Data Augmentation for Intent Recognition and Slot Filling](https://mlsanity.com/paper/2608.18689/) (2026)
- [Arabic Prompts with English Tools: A Benchmark](https://mlsanity.com/paper/2601.05101/) (2026)
- [Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation](https://mlsanity.com/paper/2604.03395/) (2026)
- [ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation](https://mlsanity.com/paper/2608.30475/) (2026)
- [ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts](https://mlsanity.com/paper/2609.29349/) (2026)

## Abstract (arXiv)

The emergence of ChatGPT marked a transformative milestone for Artificial Intelligence (AI), showcasing the remarkable potential of Large Language Models (LLMs) to generate human-like text. This wave of innovation has revolutionized how we interact with technology, seamlessly integrating LLMs into everyday tasks such as vacation planning, email drafting, and content creation. While English-speaking users have significantly benefited from these advancements, the Arabic world faces distinct challenges in developing Arabic-specific LLMs. Arabic, one of the languages spoken most widely around the world, serves more than 422 million native speakers in 27 countries and is deeply rooted in a rich linguistic and cultural heritage. Developing Arabic LLMs (ALLMs) presents an unparalleled opportunity to bridge technological gaps and empower communities. The journey of ALLMs has been both fascinating and complex, evolving from rudimentary text processing systems to sophisticated AI-driven models. This article explores the trajectory of ALLMs, from their inception to the present day, highlighting the efforts to evaluate these models through benchmarks and public leaderboards. We also discuss the challenges and opportunities that ALLMs present for the Arab world.