Attention-Shifting uses importance-aware suppression on unlearning data and retention enhancement on retained data via dual-loss optimization to achieve selective unlearning with better utility preservation than prior methods.
Offset unlearning for large language models
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GCD uses diffusion model priors to guide suffix search, achieving higher attack success rates with better semantic adherence and lower detection than GCG-style methods.
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.
citing papers explorer
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Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting
Attention-Shifting uses importance-aware suppression on unlearning data and retention enhancement on retained data via dual-loss optimization to achieve selective unlearning with better utility preservation than prior methods.
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Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance
GCD uses diffusion model priors to guide suffix search, achieving higher attack success rates with better semantic adherence and lower detection than GCG-style methods.
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Machine Unlearning: A Comprehensive Survey
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.