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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A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.
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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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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.