Safety Alignment via Constrained Knowledge Unlearning
Zesheng Shi, Yucheng Zhou, Jing Li — 2025-05-24 — arXiv
Summary
Proposes Constrained Knowledge Unlearning (CKU), a novel safety alignment method that identifies and preserves useful knowledge neurons while selectively pruning gradients during unlearning to remove harmful knowledge from LLMs without compromising performance.
Key Result
CKU significantly enhances model safety against jailbreak attacks while maintaining overall performance, offering superior balance between safety and utility compared to existing methods.
Source
- Link: https://arxiv.org/abs/2505.18588
- Listed in the Shallow Review of Technical AI Safety 2025 under 1 agenda(s):
- capability-removal-unlearning — Black-box safety (understand and control current model behaviour) / Iterative alignment