4 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…
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…
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…
OpenTAD: A Unified Framework and Comprehensive Study of Temporal Action Detection
Shuming Liu, Chen Zhao, Fatimah Zohra +10
Temporal action detection (TAD) is a fundamental video understanding task that aims to identify human actions and localize their temporal boundaries in videos. Although this field…