Advancing LLM Security through Better Unlearning
A comprehensive framework for auditing knowledge removal in large language models
HANKER introduces a novel approach to evaluate how effectively sensitive information can be removed from large language models, addressing critical security and privacy concerns.
- Creates holistic audit datasets through knowledge graph traversal to test unlearning effectiveness
- Employs redundancy removal to ensure comprehensive yet efficient testing
- Generates hundreds of thousands of test cases versus only hundreds in previous benchmarks
- Provides a standardized evaluation framework for LLM unlearning techniques
This research is crucial for organizations deploying LLMs that need to comply with privacy regulations, protect sensitive information, and address copyright concerns while maintaining model utility.