VLM-UnBench demonstrates that prompt-based training-free unlearning in VLMs leaves forget accuracy near the no-instruction baseline except under oracle conditions that reveal the target concept.
Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith
5 Pith papers cite this work. Polarity classification is still indexing.
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Targeting minor components in LLM representations during unlearning yields substantially better resistance to relearning attacks than prior methods.
CAP enables reversible unlearning of targeted knowledge in LLMs through optimized prompts generated via reinforcement learning, without any parameter updates.
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
CURaTE performs continual unlearning in LLMs in real time by using sentence embeddings to detect and refuse forget requests without changing model parameters, achieving effective forgetting and perfect knowledge preservation.
citing papers explorer
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Can VLMs Truly Forget? Benchmarking Training-Free Visual Concept Unlearning
VLM-UnBench demonstrates that prompt-based training-free unlearning in VLMs leaves forget accuracy near the no-instruction baseline except under oracle conditions that reveal the target concept.
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Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter
Targeting minor components in LLM representations during unlearning yields substantially better resistance to relearning attacks than prior methods.
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CAP: Controllable Alignment Prompting for Unlearning in LLMs
CAP enables reversible unlearning of targeted knowledge in LLMs through optimized prompts generated via reinforcement learning, without any parameter updates.
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Representation-Guided Parameter-Efficient LLM Unlearning
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
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CURaTE: Continual Unlearning in Real Time with Ensured Preservation of LLM Knowledge
CURaTE performs continual unlearning in LLMs in real time by using sentence embeddings to detect and refuse forget requests without changing model parameters, achieving effective forgetting and perfect knowledge preservation.