41 citations · 61 across the 12 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2024★ 2 cited
An Adversarial Perspective on Machine Unlearning for AI Safety
Jakub Łucki, Boyi Wei, Yangsibo Huang +3
Large language models are finetuned to refuse questions about hazardous knowledge, but these protections can often be bypassed. Unlearning methods aim at completely removing hazard…
cs.LG2024★ 1 cited
What is in Your Safe Data? Identifying Benign Data that Breaks Safety
Luxi He, Mengzhou Xia, Peter Henderson
Current Large Language Models (LLMs), even those tuned for safety and alignment, are susceptible to jailbreaking. Some have found that just further fine-tuning an aligned model wit…
cs.LG2024★ 2 cited
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
Boyi Wei, Kaixuan Huang, Yangsibo Huang +6
Large language models (LLMs) show inherent brittleness in their safety mechanisms, as evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This stu…