4 citations · 24 across the 29 of their papers we have counts for
8 papers · 1 filter
Toward a Safer Web: Multilingual Multi-Agent LLMs for Mitigating Adversarial Misinformation Attacks
Nouar Aldahoul, Yasir Zaki
The rapid spread of misinformation on digital platforms threatens public discourse, emotional stability, and decision-making. While prior work has explored various adversarial atta…
Detecting Hope, Hate, and Emotion in Arabic Textual Speech and Multi-modal Memes Using Large Language Models
Nouar AlDahoul, Yasir Zaki
The rise of social media and online communication platforms has led to the spread of Arabic textual posts and memes as a key form of digital expression. While these contents can be…
Benchmarking the Medical Understanding and Reasoning of Large Language Models in Arabic Healthcare Tasks
Nouar AlDahoul, Yasir Zaki
Recent progress in large language models (LLMs) has showcased impressive proficiency in numerous Arabic natural language processing (NLP) applications. Nevertheless, their effectiv…
Benchmarking the Legal Reasoning of LLMs in Arabic Islamic Inheritance Cases
Nouar AlDahoul, Yasir Zaki
Islamic inheritance domain holds significant importance for Muslims to ensure fair distribution of shares between heirs. Manual calculation of shares under numerous scenarios is co…
Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles
Chen Wei Kuo, Kevin Chu, Nouar AlDahoul +3
Bias in news reporting significantly impacts public perception, particularly regarding crime, politics, and societal issues. Traditional bias detection methods, predominantly relia…
Multitask Mayhem: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning
Essa Jan, Nouar AlDahoul, Moiz Ali +3
Recent breakthroughs in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment anal…
Self-Reflection Makes Large Language Models Safer, Less Biased, and Ideologically Neutral
Fengyuan Liu, Nouar AlDahoul, Gregory Eady +2
Previous studies proposed that the reasoning capabilities of large language models (LLMs) can be improved through self-reflection, i.e., letting LLMs reflect on their own output to…
A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles
Nouar AlDahoul, Talal Rahwan, Yasir Zaki
A quarter of US adults regularly get their news from YouTube. Yet, despite the massive political content available on the platform, to date no classifier has been proposed to ident…