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.
Meow: Memory supervised llm unlearning via inverted facts
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Machine unlearning should be restricted to dataset-defined deletion achieving retraining equivalence, while other LLM tasks require separate terminology and evaluation baselines.
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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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Position: The Term "Machine Unlearning" Is Overused in LLMs
Machine unlearning should be restricted to dataset-defined deletion achieving retraining equivalence, while other LLM tasks require separate terminology and evaluation baselines.