# Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps

arXiv: https://arxiv.org/abs/2510.13430 · HTML: https://mlsanity.com/paper/2510.13430/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 59 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 30 of 42)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://mlsanity.com/paper/2402.12840/) (2024)
- [AceGPT, Localizing Large Language Models in Arabic](https://mlsanity.com/paper/2309.12053/) (2023)
- [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://mlsanity.com/paper/2011.03080/) (2020)
- [AraGPT2: Pre-Trained Transformer for Arabic Language Generation](https://mlsanity.com/paper/2012.15520/) (2020)
- [Neural Arabic Question Answering](https://mlsanity.com/paper/1906.05394/) (2019)
- [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://mlsanity.com/paper/2212.10758/) (2022)
- [AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs](https://mlsanity.com/paper/2409.11404/) (2024)
- [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)
- [PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture](https://mlsanity.com/paper/2509.02550/) (2025)
- [YourBench: Easy Custom Evaluation Sets for Everyone](https://mlsanity.com/paper/2504.01833/) (2025)
- [Jawaher: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking](https://mlsanity.com/paper/2503.00231/) (2025)
- [Arabic Dataset for LLM Safeguard Evaluation](https://mlsanity.com/paper/2410.17040/) (2024)
- [CAMEL-Bench: A Comprehensive Arabic LMM Benchmark](https://mlsanity.com/paper/2410.18976/) (2024)
- [AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic](https://mlsanity.com/paper/2403.09017/) (2024)
- [Commonsense Reasoning in Arab Culture](https://mlsanity.com/paper/2502.12788/) (2025)
- [Fann or Flop: A Multigenre, Multiera Benchmark for Arabic Poetry Understanding in LLMs](https://mlsanity.com/paper/2505.18152/) (2025)
- [Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs](https://mlsanity.com/paper/2503.00151/) (2025)
- [MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks](https://mlsanity.com/paper/2505.03427/) (2025)
- [HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations](https://mlsanity.com/paper/2503.07833/) (2025)
- [A Survey of Large Language Models for Arabic Language and its Dialects](https://mlsanity.com/paper/2410.20238/) (2024)
- [The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology](https://mlsanity.com/paper/2506.01340/) (2025)

## Built on by (most cited first, 9 of 9)

- [AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models](https://mlsanity.com/paper/2511.14295/) (2025)
- [HalluScore: Large Language Model Hallucination Question Answering Benchmark](https://mlsanity.com/paper/2605.17007/) (2026)
- [Bridging Scientific Heritage: An Arabic--Russian Parallel Corpus and LLM Benchmark for Sustainable Knowledge Transfer](https://mlsanity.com/paper/2606.30943/) (2026)
- [ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning](https://mlsanity.com/paper/2602.17054/) (2026)
- [Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation](https://mlsanity.com/paper/2604.03395/) (2026)
- [State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation](https://mlsanity.com/paper/2604.06421/) (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)
- [Halluscoring 2026: The first shared task on llms hallucination detection and answer verification](https://mlsanity.com/paper/2609.38355/) (2026)
- [Does Modern Standard Arabic (MSA) Dominate Arabic Dialects in LLMs? A Representation-Level Analysis](https://mlsanity.com/paper/2610.11510/) (2026)

## Abstract (arXiv)

This survey provides the first systematic review of Arabic LLM benchmarks, analyzing 40+ evaluation benchmarks across NLP tasks, knowledge domains, cultural understanding, and specialized capabilities. We propose a taxonomy organizing benchmarks into four categories: Knowledge, NLP Tasks, Culture and Dialects, and Target-Specific evaluations. Our analysis reveals significant progress in benchmark diversity while identifying critical gaps: limited temporal evaluation, insufficient multi-turn dialogue assessment, and cultural misalignment in translated datasets. We examine three primary approaches: native collection, translation, and synthetic generation discussing their trade-offs regarding authenticity, scale, and cost. This work serves as a comprehensive reference for Arabic NLP researchers, providing insights into benchmark methodologies, reproducibility standards, and evaluation metrics while offering recommendations for future development.