A Survey of Large Language Models for Arabic Language and its Dialects

Hend Al-Khalifa; Malak Mashaabi; Shahad Al-Khalifa

arxiv: 2410.20238 · v2 · pith:FAXAAYZ3new · submitted 2024-10-26 · 💻 cs.CL · cs.AI

A Survey of Large Language Models for Arabic Language and its Dialects

Malak Mashaabi , Shahad Al-Khalifa , Hend Al-Khalifa This is my paper

Pith reviewed 2026-05-23 19:26 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords Arabic LLMsdialectal Arabicmodel opennessClassical ArabicModern Standard Arabicsentiment analysisnamed entity recognitionquestion answering

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The pith

Arabic LLMs concentrate on Modern Standard Arabic while dialect coverage stays limited and openness remains low.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The survey reviews large language models built for Arabic and its dialects, grouping them by architecture and by whether they handle Classical Arabic, Modern Standard Arabic, or dialectal varieties. It examines the datasets used in pre-training and measures performance on tasks including sentiment analysis, named entity recognition, and question answering. The authors evaluate each model for openness using criteria such as public code, training data, weights, and documentation, then identify the scarcity of diverse dialectal datasets as a central gap. They link greater openness directly to improved reproducibility and transparency in Arabic NLP research.

Core claim

Existing Arabic LLMs include encoder-only, decoder-only, and encoder-decoder designs that are monolingual, bilingual, or multilingual, yet most training data and evaluation focus on Modern Standard Arabic with sparse dialectal representation, and few models release the resources needed for independent verification or extension.

What carries the argument

Assessment of model openness according to the public availability of source code, training data, model weights, and documentation, combined with classification of models by architecture and language variety coverage.

If this is right

Greater collection and release of diverse dialectal datasets would expand the range of Arabic varieties that LLMs can handle effectively.
Higher openness in code, data, and weights would allow independent reproduction and incremental improvement of existing models.
Future models built with more representative data would better serve speakers of multiple Arabic dialects on downstream tasks.
Identified challenges such as dialect variation and data scarcity point to concrete opportunities for targeted data creation and model adaptation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same pattern of limited dialect coverage and closed resources may appear in surveys of LLMs for other languages that have many regional varieties.
Wider release of open Arabic models could accelerate practical applications such as machine translation or content moderation for Arabic-speaking populations.
New benchmarks that systematically test across multiple dialects would provide a clearer measure of progress than current task-focused evaluations.

Load-bearing premise

The authors' selection of papers, models, and datasets captures a sufficiently complete and unbiased picture of current Arabic LLMs.

What would settle it

A new compilation that lists many additional Arabic LLMs with either substantially wider dialectal training data or higher rates of public code and weights than those covered in the survey.

read the original abstract

This survey offers a comprehensive overview of Large Language Models (LLMs) designed for Arabic language and its dialects. It covers key architectures, including encoder-only, decoder-only, and encoder-decoder models, along with the datasets used for pre-training, spanning Classical Arabic, Modern Standard Arabic, and Dialectal Arabic. The study also explores monolingual, bilingual, and multilingual LLMs, analyzing their architectures and performance across downstream tasks, such as sentiment analysis, named entity recognition, and question answering. Furthermore, it assesses the openness of Arabic LLMs based on factors, such as source code availability, training data, model weights, and documentation. The survey highlights the need for more diverse dialectal datasets and attributes the importance of openness for research reproducibility and transparency. It concludes by identifying key challenges and opportunities for future research and stressing the need for more inclusive and representative models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a standard survey that organizes Arabic LLM work by architecture, data variety, and openness but provides no method for its literature selection.

read the letter

The main takeaway is that this paper collects existing information on LLMs for Arabic and its dialects into one document, which can serve as a reference list for people entering the subfield. It groups models by encoder-only, decoder-only, and encoder-decoder designs, distinguishes monolingual from multilingual setups, and breaks down pre-training data into Classical Arabic, Modern Standard Arabic, and Dialectal Arabic. It also reviews performance on tasks such as sentiment analysis, named entity recognition, and question answering, and it flags openness factors like code and weight availability as important for reproducibility. The emphasis on needing more diverse dialectal datasets is a reasonable observation drawn from the reviewed material. These elements give the paper a clear structure and a practical focus that some broader LLM surveys lack. The selection process for the included papers and models is not described. No search strings, databases, date cutoffs, or inclusion criteria appear in the abstract or the outlined sections, so it is impossible to verify whether recent or lower-cited dialectal work was captured or whether the set leans toward highly visible sources. That gap makes the claims about key challenges and future opportunities harder to evaluate. The paper is aimed at Arabic NLP researchers who want a consolidated starting point rather than new methods or data. It will not shift the wider field. It deserves peer review so that specialists can test the actual coverage against the literature and suggest additions where needed.

Referee Report

2 major / 2 minor

Summary. This survey paper claims to provide a comprehensive overview of LLMs for Arabic and its dialects. It reviews encoder-only, decoder-only, and encoder-decoder architectures; pre-training datasets spanning Classical Arabic, Modern Standard Arabic, and Dialectal Arabic; monolingual, bilingual, and multilingual models; performance on downstream tasks including sentiment analysis, named entity recognition, and question answering; and openness factors such as code, data, weights, and documentation availability. The paper concludes by stressing the need for more diverse dialectal datasets, the value of openness for reproducibility, and identifying key challenges and future opportunities for more inclusive models.

Significance. A well-executed survey in this area could consolidate fragmented work on Arabic LLMs, surface gaps in dialectal coverage, and encourage better practices around openness. The emphasis on reproducibility and inclusivity aligns with broader community priorities in multilingual NLP. However, the absence of a documented selection methodology means the claimed comprehensiveness cannot be verified, limiting the paper's utility as a reliable reference.

major comments (2)

[Abstract, Introduction] Abstract and Introduction: The paper repeatedly claims to deliver a 'comprehensive overview' of Arabic LLMs, architectures, datasets, and openness, yet provides no description of the literature search strategy, databases queried, search strings, inclusion/exclusion criteria, date cutoffs, or handling of preprints and low-citation works. This directly undermines the ability to assess whether the reviewed set is representative, especially for dialectal Arabic resources.
[Datasets and Models sections] Section on datasets and models (likely §3–4): Without an explicit methodology, it is impossible to determine whether coverage of recent dialectal datasets or less-cited models is systematic or inadvertently biased toward high-visibility English-centric sources, which is load-bearing for the paper's central claim of highlighting gaps and opportunities.

minor comments (2)

[References] Ensure all cited works have consistent formatting and DOIs where available; some references appear to lack full bibliographic details.
[Openness assessment] Clarify the distinction between 'openness' criteria (code vs. weights vs. data) with explicit tables or checklists for each reviewed model to improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and for highlighting the importance of methodological transparency in survey papers. We agree that explicitly documenting the literature search process will strengthen the manuscript's credibility as a reliable reference and better support its claims of comprehensiveness. We will revise the paper accordingly.

read point-by-point responses

Referee: [Abstract, Introduction] Abstract and Introduction: The paper repeatedly claims to deliver a 'comprehensive overview' of Arabic LLMs, architectures, datasets, and openness, yet provides no description of the literature search strategy, databases queried, search strings, inclusion/exclusion criteria, date cutoffs, or handling of preprints and low-citation works. This directly undermines the ability to assess whether the reviewed set is representative, especially for dialectal Arabic resources.

Authors: We acknowledge that the current version of the manuscript does not include an explicit description of the literature search methodology. To address this concern, we will add a new subsection (e.g., 'Literature Review Methodology') early in the paper that details the search strategy. This will include the databases and repositories queried (Google Scholar, arXiv, ACL Anthology), search strings used (combinations of terms such as 'Arabic LLM', 'dialectal Arabic models', 'Arabic pretraining datasets'), inclusion/exclusion criteria (focus on models and datasets specifically targeting Arabic or its dialects, published or posted by October 2024), handling of preprints versus peer-reviewed works, and any steps taken to ensure coverage of lower-visibility dialectal resources. This addition will enable readers to evaluate the representativeness of the surveyed literature. revision: yes
Referee: [Datasets and Models sections] Section on datasets and models (likely §3–4): Without an explicit methodology, it is impossible to determine whether coverage of recent dialectal datasets or less-cited models is systematic or inadvertently biased toward high-visibility English-centric sources, which is load-bearing for the paper's central claim of highlighting gaps and opportunities.

Authors: We agree that the absence of a documented methodology makes it difficult to assess the systematic nature of coverage in the datasets and models sections. The new methodology subsection described above will directly clarify our approach to identifying and selecting works for these sections, including how we sought out recent dialectal datasets and less-cited models beyond high-visibility sources. This will support the paper's discussion of gaps in dialect coverage and help substantiate claims about opportunities for more inclusive models. revision: yes

Circularity Check

0 steps flagged

No circularity: survey aggregates external literature without internal derivations or self-referential reductions

full rationale

This paper is a literature survey reviewing external work on Arabic LLMs, architectures, datasets, and openness factors. It contains no equations, fitted parameters, predictions, or derivation chains that could reduce to the paper's own inputs by construction. No self-citation is used to justify a uniqueness theorem or ansatz, and the central claims rest on cited external sources rather than internal redefinitions. The literature selection process, while undocumented in the provided text, does not constitute circularity under the enumerated patterns because the paper makes no claim that its conclusions are mathematically forced by its own structure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey paper the work introduces no new free parameters, axioms, or invented entities; it relies entirely on existing published models and datasets.

pith-pipeline@v0.9.0 · 5682 in / 1201 out tokens · 29384 ms · 2026-05-23T19:26:30.234736+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

HalluScore: Large Language Model Hallucination Question Answering Benchmark
cs.CL 2026-05 unverdicted novelty 7.0

HalluScore is a curated Arabic QA dataset with 827 questions, ground-truth evidence, and human annotations used to measure hallucination rates across 17 LLMs.
State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation
cs.CL 2026-04 unverdicted novelty 4.0

Arabic-DeepSeek-R1 sets new state-of-the-art results on the Open Arabic LLM Leaderboard by combining sparse MoE fine-tuning with culturally-informed CoT distillation on a controlled bilingual dataset.

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages · cited by 2 Pith papers · 1 internal anchor

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S. A. Chowdhury, A. Abdelali, K. Darwish, J. Soon-Gyo, J. Salminen, and B. J. Jansen, “Qarib,” in Proceedings of the Fifth Arabic Natural Language Processing Workshop, I. Zitouni, M. Abdul-Mageed, H. Bouamor, F. Bougares, M. El-Haj, N. Tomeh, and W. Zaghouani, Eds., Barcelona, Spain (Online): Association for Computational Linguistics, Dec. 2020, pp. 226–2...

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