activity
20162026
most citedArabic Offensive Language on Twitter: Analysis and Experiments

86 citations · 332 across the 36 of their papers we have counts for

collaborators
Showing 2026 · cs.CLShow all

6 papers · 2 filters

cs.CL2026

AraYoungVoices: A Diverse L1/L2 Corpus of Arabic Child and Adolescent Speech

Shammur Absar Chowdhury, Zien Sheikh Ali, Houssam Eddine-Othman Lachemat +1

State-of-the-art ASR systems primarily target native adult speech, leading to substantial performance gaps for children, adolescents, and L2 speakers. We introduce AraYoungVoices,…

cs.CL2026

Almieyar: A Culturally Grounded Benchmark for Multi-Dialect Arabic Speech Recognition

Omid Ghahroodi, Anas Madkoor, Dima Faris Al Saudi +37

Arabic speech technology has largely focused on Modern Standard Arabic, leaving the living dialects spoken by hundreds of millions under-served. We introduce ALMIEYAR, a culturally…

cs.CL2026

ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts

Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi +5

ArGuard is a shared task on harmful content detection in Arabic memes and LLM prompts. It includes two tracks: Track A focuses on multimodal hate detection in Arabic memes, while T…

cs.CL2026

Fanar 2.0: Arabic Generative AI Stack

FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34

We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…

cs.CL2026

Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA

Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh +7

Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic sett…

cs.CL2026★ 1 cited

From RAG to Agentic RAG for Faithful Islamic Question Answering

Gagan Bhatia, Hamdy Mubarak, Mustafa Jarrar +8

Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. Yet standard MCQ/MRC-style e…