arXiv:2606.27683v1 Announce Type: new Abstract: Edge devices increasingly invoke large language models (LLMs) through API services for context aware edge intelligence, while edge generated data may be collected to improve LLMs and may introduce sensitive, copyrighted, harmful, or outdated information into model behavior. Machine unlearning offers a practical way to remove the influence of undesired data without retraining LLMs. However, existing methods still face two gaps. The first is API only black box access, where target model parameters and internal logits are unavailable. The second is
The proliferation of LLMs via API-only access and increasing concerns about data privacy and intellectual property are driving urgent research into practical unlearning methods.
This research addresses a critical limitation in current LLM deployment, enabling dynamic content moderation and regulatory compliance for sensitive data without expensive retraining.
The ability to unlearn specific data from black-box LLMs via API access could significantly enhance the deployability and ethical governance of these models in sensitive applications.
- · LLM API providers
- · Edge device manufacturers
- · Regulatory bodies
- · Users concerned about data privacy
- · Malicious actors attempting data poisoning
- · Companies with poor data governance practices
Wider adoption of LLMs in highly regulated industries becomes feasible due to enhanced data control.
New business models emerge around auditing and 'unlearning as a service' for AI models.
Increased public trust in AI systems due to transparent and controllable data influence mechanisms.
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Read at arXiv cs.LG