arXiv:2507.04219v5 Announce Type: replace-cross Abstract: Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their fine-tuning data. We argue this not only risks reinforcing exposure to sensitive data, but also fundamentally contradicts the principle of minimizing its use. As a remedy, we propose a novel unlearning method-Partial Model Collapse (PMC), which does not require unlearning targets in the unlearning objective. Our approach is inspired by recent observations that training generative models on their own generations lead
The increasing deployment and scrutiny of large language models heighten the criticality of data privacy and the ability to selectively remove sensitive information.
This research introduces a novel, more robust method for machine unlearning that addresses fundamental risks associated with current techniques, impacting the ethical and secure development of LLMs.
The proposed 'Partial Model Collapse' method shifts the paradigm for unlearning by not directly optimizing on the private information, reducing exposure risk, and potentially improving unlearning efficacy.
- · AI developers
- · Data privacy advocates
- · LLM users
- · Developers relying on current unlearning methods
Improved methods for ethical AI development and compliance with data protection regulations will become more viable.
Public trust in AI systems handling sensitive data could increase, fostering broader adoption in regulated industries.
This could enable new business models built on highly customizable and privacy-preserving AI, where specific data can be dynamically 'unlearned' or excluded.
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Read at arXiv cs.AI