AraDetox is a newly released multi-dialect Arabic detoxification dataset containing 10,500 harmful social‑media posts and 84,000 detoxified rewrites generated by GPT‑5 and Gemini 2.5 Flash across Modern Standard Arabic, Gulf, Levantine, and Egyptian Arabic. Human evaluation and automatic analyses confirm that the rewrites effectively remove harmful language while preserving meaning, lexical change, and dialectal style. The dataset is publicly available to support future research in Arabic detoxification, safe text generation, and multi‑dialect NLP.
By Mo El-Haj
arXiv:2609.22796v1 Announce Type: new
Abstract: Dialectal Arabic machine translation (MT) remains challenging despite recent progress in Arabic language technologies, particularly because effective t...
By Abdellah El Mekki, AbdelRahim A. Elmadany, Samar M. Magdy, Saad Ezzini, Mo El-Haj, Mustafa Jarrar, Zaid Alyafeai, Bernard Ghanem, Muhammad Abdul-Mageed
Arabic Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large language models (LLMs). Despite this growth...
arXiv:2608.23421v1 Announce Type: new
Abstract: Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large l...
By Mullosharaf K. Arabov
The paper surveys the state of Explainable AI (XAI) in Arabic NLP, highlighting three gaps: a method gap where Arabic XAI relies mainly on limited post‑hoc techniques; a task gap with most work focused on classification tasks and little on generation, retrieval, or dialogue; and a linguistic gap where explanations rarely address Arabic‑specific phenomena such as morphology, dialects, and diglossia. It proposes a taxonomy of tasks, methods, linguistic units, and evaluation practices, and outlines a research agenda for linguistically grounded Arabic XAI.
By Salima Lamsiyah, Ruslan Mitkov
arXiv:2606.13218v2 Announce Type: replace
Abstract: Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and m...
By Junhong Liang, Noor Abo Mokh, Bashar Alhafni
The study investigates whether Modern Standard Arabic (MSA) dominates internal representations in large language models (LLMs) when generating Arabic, even if prompted with dialectal forms. By extending a language-dominance framework to 26 Arabic varieties, the authors find no evidence that MSA serves as a dominant internal representation; instead, dialect representations are densely overlapping, with mutual information dropping sharply compared to more distinct languages. The strongest separability effects vary across layers and architectures, indicating that generation bias toward MSA does not necessarily reflect internal representational dominance.
By Abdu Sallouh, Nicholas Popovi\v{c}, Michael F\"{a}rber
The paper introduces a rubric-based benchmark to evaluate Saudi Arabic dialect and cultural competence in large language models. It comprises 31 expert-authored prompts covering idiomatic, pragmatic, lexical, and culturally embedded aspects, each paired with an expert-established ground truth. Four state-of-the-art models were scored, revealing that none exceeded 55% accuracy and that ambiguous framing was the most common error type.
By Ghassan Al-Sumaidaee, Sajjad Abdoli, Ahmed Rashad, Maxim Legg
arXiv:2609.23955v1 Announce Type: new
Abstract: Previous work on Egyptian Arabic in NLP has focused largely on the prestigious Cairene Egyptian Arabic (CEA) dialect, resulting in a lack of representa...
By Mai Mohamed Eida, Ryan Dolan, Paul de Nijs, Jonathan Dunn
EDRAC is the first large‑scale benchmark for dialectal Arabic machine reading comprehension and generative question answering, covering five major dialects—Egyptian, Moroccan, Emirati, Syrian, and Saudi. It contains 499 passages from naturally spoken interactions and 4,977 QA pairs produced via a human–LLM collaborative pipeline. The benchmark evaluates Arabic‑centric and multilingual large language models, revealing gaps between semantic answer quality and dialectal fidelity and underscoring limitations of current evaluation metrics for dialectal Arabic generation.
By Noor Abo Mokh, Kirill Chirkunov, Teresa Lynn, Nizar Habash, Reham Marzouk, Malik H. Altakrori, Younes Samih, Muhammed Abu Odeh, Nour Rabih, Rahaf Alshahrani, Hamad Alshehhi, Hamdan Al-Ali, Muhra Almahri, Besher Hassan, Mohamed Anwar, Abed Alhakim Freihat, Preslav Nakov, Alham Fikri Aji
Previous work on Egyptian Arabic in NLP has focused largely on the prestigious Cairene Egyptian Arabic (CEA) dialect, resulting in a lack of representation for the less prestigious Sa'idi Egyptian Ara...
The paper introduces MADARTB, the first large-scale parallel Arabic dependency treebank covering five city dialects—Beirut, Cairo, Doha, Rabat, and Tunis—alongside Modern Standard Arabic. It contains 5,000 sentences annotated with the CATiB dependency scheme and shows that while overall construction profiles are similar across varieties, local syntactic structures often diverge, indicating significant structural variation. A supervised parser achieves 87.5% LAS, outperforming the best LLM setup at 85.7% LAS across all six varieties, and the authors release the annotations and models publicly.
By Noor Abo Mokh, Mohamed Adel, Jamila El-Gizuli, Go Inoue, Ahmed Elshabrawy, Nizar Habash