Securing LLMs Against Data Leakage
Using Activation Steering to Reduce Memorization While Preserving Performance
This research introduces a novel approach to mitigating privacy risks in Large Language Models by addressing problematic memorization of training data.
- Applies activation steering techniques to directly intervene in model activations
- Demonstrates reduction in data regurgitation without sacrificing model capabilities
- Provides a technical solution for preventing leaks of sensitive or copyrighted content
- Offers a balanced approach to the memorization-generalization tradeoff
This work has significant implications for enterprise AI security, helping organizations deploy LLMs with reduced risk of exposing private information or violating copyright protections.