arXiv:2605.24614v1 Announce Type: new Abstract: Large language model (LLM) unlearning has emerged as a crucial post-hoc mechanism for privacy protection and AI safety, yet auditing whether target knowledge is truly erased remains challenging. Existing output-level metrics fail to detect when this knowledge remains recoverable from internal representations. Recent white-box studies reveal such residual knowledge but often rely on auxiliary training or dataset-specific adaptations, leaving no generalizable metric. To address these limitations, we propose the Unlearning Depth Score (UDS), a metri
As AI models become more pervasive and powerful, critical issues like privacy, safety, and responsible deployment necessitate robust auditing mechanisms for unlearning capabilities.
A reliable metric for LLM unlearning is crucial for regulatory compliance, establishing trust in AI systems, and ensuring AI safety by proving sensitive information can be truly erased.
The proposed Unlearning Depth Score (UDS) offers a generalizable, quantitative method to assess the true 'erasure' of knowledge within LLMs, moving beyond superficial output-level metrics.
- · AI Safety Researchers
- · Regulatory Bodies
- · Companies deploying LLMs
- · LLM developers without robust unlearning capabilities
- · Proprietary AI models with opaque internal states
The adoption of UDS or similar metrics will enable more effective auditing of LLM unlearning processes.
This improved auditing capability will likely drive further innovation in unlearning techniques and responsible AI development.
Greater confidence in unlearning could mitigate some privacy and safety concerns, potentially accelerating broader deployment of large language models in sensitive applications.
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Read at arXiv cs.CL