most citedMulti-task Learning with Active Learning for Arabic Offensive Speech Detection

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Large Language Models Hallucination: A Comprehensive Survey

Aisha Alansari, Hamzah Luqman

Large language models (LLMs) have transformed natural language processing, achieving remarkable performance across diverse tasks. However, their impressive fluency often comes at t…

cs.CL2025

AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs

Aisha Alansari, Hamzah Luqman

Recently, extensive research on the hallucination of the large language models (LLMs) has mainly focused on the English language. Despite the growing number of multilingual and Ara…

cs.CL20251 cited

Multi-task Learning with Active Learning for Arabic Offensive Speech Detection

Aisha Alansari, Hamzah Luqman

The rapid growth of social media has amplified the spread of offensive, violent, and vulgar speech, which poses serious societal and cybersecurity concerns. Detecting such content…

cs.CL2025

AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP

Ahmed Hasanaath, Aisha Alansari, Ahmed Ashraf +3

Large language models (LLMs) have shown remarkable progress in reasoning abilities and general natural language processing (NLP) tasks, yet their performance on Arabic data, charac…

cs.CL2025

Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset

Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki +34

Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a…