37 citations · 66 across the 10 of their papers we have counts for
12 papers
Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD?
Razan El Mais, Ali Chehab, Ibrahim Issa +1
Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Many decoder-only LLMs employ…
Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology
Hadi Hasan, Safaa Salman, Lama Sleem +2
Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial w…
Toward Trustworthy Large Language Model Agents in Healthcare
Hadi Hasan, Safaa Salman, Adam Tai Abou Dargham +2
Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high administrative overhead. These ine…
Chained Prompting for Better Systematic Review Search Strategies
Fatima Nasser, Fouad Trad, Ammar Mohanna +2
Systematic reviews require the use of rigorously designed search strategies to ensure both comprehensive retrieval and minimization of bias. Conventional manual approaches, althoug…
On the Effectiveness of Membership Inference in Targeted Data Extraction from Large Language Models
Ali Al Sahili, Ali Chehab, Razane Tajeddine
Large Language Models (LLMs) are prone to memorizing training data, which poses serious privacy risks. Two of the most prominent concerns are training data extraction and Membershi…
Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection
Fouad Trad, Ali Chehab
Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in specialized tasks. However, its effectiv…
CLASP: Cost-Optimized LLM-based Agentic System for Phishing Detection
Fouad Trad, Ali Chehab
Phishing websites remain a significant cybersecurity threat, necessitating accurate and cost-effective detection mechanisms. In this paper, we present CLASP, a novel system that ef…
Detecting Quishing Attacks with Machine Learning Techniques Through QR Code Analysis
Fouad Trad, Ali Chehab
The rise of QR code-based phishing ("Quishing") poses a growing cybersecurity threat, as attackers increasingly exploit QR codes to bypass traditional phishing defenses. Existing d…
Streamlining Systematic Reviews: A Novel Application of Large Language Models
Fouad Trad, Ryan Yammine, Jana Charafeddine +4
Systematic reviews (SRs) are essential for evidence-based guidelines but are often limited by the time-consuming nature of literature screening. We propose and evaluate an in-house…
Large Multimodal Agents for Accurate Phishing Detection with Enhanced Token Optimization and Cost Reduction
Fouad Trad, Ali Chehab
With the rise of sophisticated phishing attacks, there is a growing need for effective and economical detection solutions. This paper explores the use of large multimodal agents, s…
To Ensemble or Not: Assessing Majority Voting Strategies for Phishing Detection with Large Language Models
Fouad Trad, Ali Chehab
The effectiveness of Large Language Models (LLMs) significantly relies on the quality of the prompts they receive. However, even when processing identical prompts, LLMs can yield v…
Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications
Fouad Trad, Ali Chehab
The success of Large Language Models (LLMs) has led to a parallel rise in the development of Large Multimodal Models (LMMs), which have begun to transform a variety of applications…