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.
Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions
3 Pith papers cite this work. Polarity classification is still indexing.
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Survey identifying technical and supply-chain barriers to GDPR data subject rights in ML, with new framing of 'models in the dark' for downstream opacity.
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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.
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Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains
Survey identifying technical and supply-chain barriers to GDPR data subject rights in ML, with new framing of 'models in the dark' for downstream opacity.
- LLM Harms: A Taxonomy and Discussion