49 citations · 93 across the 28 of their papers we have counts for
30 papers
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…
Rethinking Safety in LLM Fine-tuning: An Optimization Perspective
Minseon Kim, Jin Myung Kwak, Lama Alssum +5
Fine-tuning language models is commonly believed to inevitably harm their safety, i.e., refusing to respond to harmful user requests, even when using harmless datasets, thus requir…
Shh, don't say that! Domain Certification in LLMs
Cornelius Emde, Alasdair Paren, Preetham Arvind +6
Large language models (LLMs) are often deployed to perform constrained tasks, with narrow domains. For example, customer support bots can be built on top of LLMs, relying on their…
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging
Kumail Alhamoud, Yasir Ghunaim, Motasem Alfarra +5
For medical imaging AI models to be clinically impactful, they must generalize. However, this goal is hindered by (i) diverse types of distribution shifts, such as temporal, demogr…
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
Hasan Abed Al Kader Hammoud, Umberto Michieli, Fabio Pizzati +4
Merging Large Language Models (LLMs) is a cost-effective technique for combining multiple expert LLMs into a single versatile model, retaining the expertise of the original ones. H…
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
Yibo Yang, Xiaojie Li, Motasem Alfarra +4
Relieving the reliance of neural network training on a global back-propagation (BP) has emerged as a notable research topic due to the biological implausibility and huge memory con…