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Mustafa Jarrar

ACL 2026×3评测与基准社会与人本多语言与语言学数据与预训练强化学习与后训练
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0 一作 · 3 篇 · ACL 2026

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2026 顶会论文

3
作者位次 #44/47ACL 2026Poster· Natural Language Processing

Alexandria: A Multi-Domain Dialectal A rabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLM s

Abdellah EL Mekki · Samar M. Magdy · Houdaifa Atou · Ruwa AbuHweidi · Baraah Qawasmeh · Omer Nacar · Thikra Al-hibiri · Razan Saadie · Hamzah A. Alsayadi · Nadia Ghezaiel Hammouda · Alshima Mohammed Alkhazimi · Aya Hamod · Al-Yas Yaqoob Al-Ghafri · Wesam El-Sayed · Asila Ismail al Sharji · Mohamad Ballout · Anas Belfathi · Karim Ghaddar · Serry Sibaee · Alaa Aoun · Aeej Mohammed Aseri · Lina Abureesh · Ahlam Bashiti · Majdal Yousef · Abdulaziz Hafiz · Yehdih Mohamed · Emira Hamedtou · Brakehe Emehah · Rahaf Alhamouri · Youssef Nafea · Aya El Aatar · Walid Al-Dhabyani · Emhemed S. Hamed · Sara Shatnawi · Fakhraddin Alwajih · Khalid Elkhidir · Ashwag Alasmari · Abdurrahman Gerrio · Omar Said Alshahri · AbdelRahim A. Elmadany · Ismail Berrada · Amir Azad Adli Al-kathiri · Fadi Zaraket · Mustafa Jarrar · Yahya Mohamed EL Hadj · Hassan Alhuzali · Muhammad Abdul-Mageed

摘要

Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than Modern Standard Arabic (MSA). Despite this, machine translation (MT) systems often generalize poorly to dialectal input, limiting their utility for millions of speakers. We introduce Alexa...

社会与人本多语言与语言学评测与基准OpenReview 项目页
作者位次 #2/2ACL 2026Poster· Natural Language Processing

A dab NER : A rabic Digital Archive Books with Nested Entity Recognition

Aya Mourad · Mustafa Jarrar

摘要

Most studies on Arabic Named Entity Recognition (NER) have focused on news texts and social media posts, while the large and rich corpus of literary Arabic books has been underrepresented. We introduce AdabNER, the first large-scale nested NER dataset for Modern Standard Arabic (MSA) literary tex...

数据与预训练强化学习与后训练评测与基准OpenReview 项目页
作者位次 #3/11ACL 2026Findings· Findings of ACL

From RAG to Agentic RAG for Faithful Islamic Question Answering

Gagan Bhatia · Hamdy Mubarak · Mustafa Jarrar · George Mikros · Fadi Zaraket · Mahmoud Alhirthani · Mutaz al-Khatib · Logan Cochrane · Kareem Mohamed Darwish · Rashid Yahiaoui · Firoj Alam

摘要

Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. Yet standard MCQ/MRC-style evaluations do not capture key real-world failure modes, notably free-form hallucinations and the ability to abstain w...

评测与基准检索与知识智能体与工具OpenReview 项目页

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