ATWU jointly optimizes model parameters and token weights via a linear scorer on hidden states, recovering oracle forget-specific tokens under a separation condition and achieving SOTA forget-retain trade-offs on TOFU and RWKU.
Not every token needs forgetting: Selective unlearning balancing forgetting and utility in large language models
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Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance
ATWU jointly optimizes model parameters and token weights via a linear scorer on hidden states, recovering oracle forget-specific tokens under a separation condition and achieving SOTA forget-retain trade-offs on TOFU and RWKU.