Existing LLM unlearning methods mainly alter dominant representation components, leaving minor components largely unchanged.
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We discover that existing unlearning methods predominantly optimize along dominant components, leaving minor components largely unchanged.
Paper's first empirical finding about representation geometry of unlearned models.
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