35 citations · 145 across the 18 of their papers we have counts for
4 papers · 1 filter
Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods
Jai Doshi, Asa Cooper Stickland
Large language model unlearning aims to remove harmful information that LLMs have learnt to prevent their use for malicious purposes. LLMU and RMU have been proposed as two methods…
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
Abhay Sheshadri, Aidan Ewart, Phillip Guo +8
Large language models (LLMs) can often be made to behave in undesirable ways that they are explicitly fine-tuned not to. For example, the LLM red-teaming literature has produced a…
Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMs
Sara Price, Arjun Panickssery, Sam Bowman +1
Backdoors are hidden behaviors that are only triggered once an AI system has been deployed. Bad actors looking to create successful backdoors must design them to avoid activation d…
Steering Without Side Effects: Improving Post-Deployment Control of Language Models
Asa Cooper Stickland, Alexander Lyzhov, Jacob Pfau +2
Language models (LMs) have been shown to behave unexpectedly post-deployment. For example, new jailbreaks continually arise, allowing model misuse, despite extensive red-teaming an…