Selective Forgetting in AI
A Framework for Agentic LLM Unlearning
ALU framework enables controlled information removal from large language models without requiring access to model weights.
- Balances effective unlearning with preserved model utility
- Computationally feasible approach using agent-based techniques
- Defends against various jailbreaking attempts
- Enhances security compliance and privacy protection
Security Impact: This approach provides crucial capabilities for AI regulation, safety protocols, and preventing security exploits through selective information suppression, while maintaining overall model performance.