19 citations · 33 across the 6 of their papers we have counts for
4 papers · 1 filter
In-Context Learning Can Re-learn Forbidden Tasks
Sophie Xhonneux, David Dobre, Jian Tang +2
Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vulnerabilities. One perspective on LLM safe…
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space
Leo Schwinn, David Dobre, Sophie Xhonneux +2
Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be directly transferred to closed-source models.…
Raising the Bar for Certified Adversarial Robustness with Diffusion Models
Thomas Altstidl, David Dobre, Björn Eskofier +2
Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirical methods such as adversarial training,…
Dissecting adaptive methods in GANs
Samy Jelassi, David Dobre, Arthur Mensch +2
Adaptive methods are a crucial component widely used for training generative adversarial networks (GANs). While there has been some work to pinpoint the "marginal value of adaptive…