Representation-Aware Unlearning via Activation Signatures: From Suppression to Entity-Signature Erasure
arXiv:2601.10566v5 Announce Type: replace-cross Abstract: Entity-level unlearning is usually evaluated by what a model says: whether it stops naming the target, refuses a query, or shifts a Truth Ratio distribution. These output-level tests, however, do not show whether a subject's internal representation has been attenuated. We introduce the Entity Representation Unlearning Framework (ERUF), a representation-aware framework that mines subject-specific activation signatures, suppresses the corresponding activation direction, and distills the behavior into LoRA parameters. Among evaluated basel
The increasing sophistication and widespread deployment of AI models necessitate more advanced and precise methods for controlling their behavior and data representations, especially concerning sensitive information or biased outputs.
This development offers a technical pathway to address critical issues like data privacy, bias mitigation, and intellectual property protection within large AI models, moving beyond superficial output-level fixes.
The ability to unlearn specific entities at a representational level allows for more robust and verifiable AI safety and ethical guidelines, potentially altering how AI models are trained, deployed, and regulated.
- · AI Safety Researchers
- · Companies with Data Privacy Concerns
- · Ethical AI Developers
- · Regulators
- · AI Models with Unaddressable Embedded Biases
- · Data Exploiters
AI models can be more effectively audited and modified to remove unwanted information or biases from their internal representations.
This could lead to legal frameworks and industry standards requiring 'right to be forgotten' or bias removal capabilities at the model's core, rather than just at its output.
The precision of unlearning could enable more sophisticated fine-tuning and personalization of foundation models for specific, sensitive applications, while maintaining regulatory compliance and trust.
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Read at arXiv cs.LG