arXiv:2606.18473v1 Announce Type: new Abstract: Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledge to forget and knowledge to retain is often unclear, since related and even distant information may be entangled in the model. In this paper, we study LLM unlearning from a data-centric perspective and measure how unlearning effects propagate from the forget set to same-domain and distant-domain knowledge. We find a consistent decay pattern: collateral damage is strong
The increasing focus on data privacy, copyright, and ethical AI development is driving the need for robust unlearning mechanisms in large language models.
This paper highlights a critical challenge in AI governance: the unintended impact of unlearning specific knowledge, which could compromise model utility and raise new ethical dilemmas.
The understanding of how knowledge is entangled within LLMs expands, necessitating more sophisticated approaches to compliance and model remediation, moving beyond simple data deletion.
- · AI Governance Researchers
- · Data Privacy Consultants
- · Ethical AI Framework Developers
- · LLM Developers (without advanced unlearning techniques)
- · Companies with weak data governance
- · Litigants claiming full data removal
Increased complexity and cost in deploying and maintaining LLMs due to the need for collateral damage assessment during unlearning.
Development of new LLM architectures or training methodologies specifically designed to mitigate knowledge entanglement for easier unlearning.
Potential for new regulatory standards requiring 'unlearnability' as a core feature of deployable AI models, impacting market access for non-compliant systems.
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Read at arXiv cs.CL