418 citations · 578 across the 22 of their papers we have counts for
8 papers · 1 filter
Unforgotten Safety: Preserving Safety Alignment of Large Language Models with Continual Learning
Lama Alssum, Hani Itani, Hasan Abed Al Kader Hammoud +3
The safety alignment of large language models (LLMs) is becoming increasingly important with their democratization. In this paper, we study the safety degradation that comes with a…
AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models
Mohammad Zbeeb, Hasan Abed Al Kader Hammoud, Sina Mukalled +5
We present AraLingBench: a fully human annotated benchmark for evaluating the Arabic linguistic competence of large language models (LLMs). The benchmark spans five core categories…
Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning
Lama Alssum, Hasan Abed Al Kader Hammoud, Motasem Alfarra +2
Deep neural networks suffer from catastrophic forgetting, where performance on previous tasks degrades after training on a new task. This issue arises due to the model's tendency t…
Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
Hasan Abed Al Kader Hammoud, Mohammad Zbeeb, Bernard Ghanem
We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong AREN teacher…
Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic
Mohammad Zbeeb, Hasan Abed Al Kader Hammoud, Bernard Ghanem
Large language models often require costly optimization, such as reinforcement learning, to master complex reasoning tasks. This work demonstrates that reasoning ability, once lear…
An Embarrassingly Simple Defense Against LLM Abliteration Attacks
Harethah Abu Shairah, Hasan Abed Al Kader Hammoud, Bernard Ghanem +1
Large language models (LLMs) are typically aligned to refuse harmful instructions through safety fine-tuning. A recent attack, termed abliteration, identifies and suppresses the si…
DiffCLIP: Differential Attention Meets CLIP
Hasan Abed Al Kader Hammoud, Bernard Ghanem
We propose DiffCLIP, a novel vision-language model that extends the differential attention mechanism to CLIP architectures. Differential attention was originally developed for larg…
On the Importance of Pretraining Data Alignment for Atomic Property Prediction
Yasir Ghunaim, Hasan Abed Al Kader Hammoud, Bernard Ghanem
This paper challenges the recent paradigm in atomic property prediction that links progress to growing dataset sizes and computational resources. We show that pretraining on a care…