arXiv:2606.17168v1 Announce Type: new Abstract: Making large language models (LLMs) deeply forget specific knowledge and values without sacrificing general capabilities remains a central challenge in unlearning. However, current methods are easily reversed by fine-tuning or few-shot prompting, suggesting their forgetting is only shallow. We identify the root cause. Existing methods target representations shared with both the retain set and the subspace recovered by a fine-tuning attacker, making unlearning both disruptive to general capabilities and easy to reverse. We propose RepSelect (Repre
The rapid advancement and deployment of LLMs necessitate robust methods for managing and modifying their learned knowledge, especially concerning sensitive data or outdated information.
Effective unlearning methods are crucial for regulatory compliance, ethical AI deployment, and maintaining user trust by preventing models from retaining or reproducing unwanted data.
The ability to 'deeply' unlearn rather than superficially forget specific knowledge allows for more secure and adaptable LLM applications, potentially reducing risks associated with data privacy and bias.
- · AI developers
- · Regulatory bodies
- · Companies deploying LLMs
- · Users concerned about data privacy
- · Malicious actors
- · Developers relying on superficial unlearning methods
Improved compliance and security for LLMs, enabling their use in more sensitive applications.
Increased trust in AI systems and potentially new regulatory frameworks for 'right to be forgotten' in AI models.
New research directions in foundational AI around adaptive learning and dynamic knowledge management.
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Read at arXiv cs.CL