The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology
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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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 2 Jun 2025.
Builds on
22 of 31 references.
References · 22
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10 more
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Citing papers per year: 2025: 3; 2026: 9.
12 papers.
- 2025Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gapscited by 9
- 2026From RAG to Agentic RAG for Faithful Islamic Question Answeringcited by 9
- 2025Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scalecited by 5
- 2026MENASpeechBank: A Reference Voice Bank with Persona-Conditioned Multi-Turn Conversations for AudioLLMscited by 3
- 2026Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Modelscited by 2
- 2025AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Modelscited by 1
- 2026Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadithcited by 1
- 2026Aslema at NADI 2026: Data Augmentation for Intent Recognition and Slot Fillingcited by 1
- 2026Arabic Prompts with English Tools: A Benchmarkcited by 0
- 2026Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluationcited by 0
- 2026ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluationcited by 0
- 2026ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Promptscited by 0
- 2026ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts24 Sep 2026
- 2026ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation31 Aug 2026
- 2026Aslema at NADI 2026: Data Augmentation for Intent Recognition and Slot Filling19 Aug 2026
- 2026Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation3 Apr 2026
- 2026Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith25 Mar 2026
- 2026Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models5 Feb 2026
- 2026MENASpeechBank: A Reference Voice Bank with Persona-Conditioned Multi-Turn Conversations for AudioLLMs3 Feb 2026
- 2026From RAG to Agentic RAG for Faithful Islamic Question Answering12 Jan 2026
- 2026Arabic Prompts with English Tools: A Benchmark8 Jan 2026
- 2025AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models18 Nov 2025
- 2025Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps15 Oct 2025
- 2025Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale17 Sep 2025