arXiv:2605.27138v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific data from trained language models. In real-world deployments, unlearning requests often arrive sequentially, which challenges existing fine-tuning-based methods: fine-tuning each request is costly, accumulates utility loss, and may cause cross-request interference. To address these issues, we propose ICCU (In-Context Continual Unlearning), an in-context continual unlearning framework that induces readable refusal rules from unlearning datasets and applies them at inference time either as
The increasing deployment of large language models in sensitive applications necessitates robust and efficient unlearning mechanisms as privacy regulations and data governance become more stringent.
This paper addresses a fundamental challenge in model governance and ethical AI, impacting regulatory compliance, data privacy, and the long-term utility of continuously updated AI systems.
The ability to efficiently remove the influence of specific data from AI models at inference time, sequentially and without significant utility loss, changes how models can be managed and adapted post-deployment.
- · AI developers and deployers
- · Data privacy regulators
- · Users concerned about data retention
- · Organizations relying on static, immutable AI models
- · Methods requiring costly retraining for unlearning
AI models can be more dynamically updated and audited for compliance, reducing operational overhead for data removal requests.
Improved model unlearning could foster greater public trust in AI systems, especially in privacy-sensitive domains.
The development of 'refusal rules' might lead to more interpretable and controllable AI behavior, opening new avenues for explainable AI.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI