ReLearn introduces a data augmentation and fine-tuning pipeline that enables more effective targeted unlearning in large language models while preserving linguistic coherence.

• Overcomes limitations of reverse optimization methods that degrade model performance
• Maintains response fluency and relevance while removing targeted information
• Implements more comprehensive evaluation metrics beyond just contextual forgetting
• Balances security requirements with maintaining model quality

This research addresses critical security concerns by providing a more sophisticated approach to removing sensitive information from AI models without compromising their overall utility and linguistic capabilities.

ReLearn: Unlearning via Learning for Large Language Models