aiqbench analysis
This research introduces PALU, a novel framework for machine unlearning that optimizes entropy specifically within sensitive prefixes and top-k logits rather than indiscriminately across the entire model. By localizing the forgetting process to critical causal links, the method significantly reduces collateral damage to general utility while maintaining superior efficacy compared to existing global approaches. The findings offer a more efficient path forward for privacy-preserving LLMs by minimizing redundant optimization in content-agnostic regions of the parameter space.
Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning
arXiv:2601.03190v4 Announce Type: replace Abstract: Machine unlearning aims to forget sensitive knowledge from Large Language Models (LLMs) while maintaining general utility. However, existing approaches typically treat all tokens in a response indiscriminately and enforce uncertainty over the entire vocabulary. This global treatment results in unnecessary utility degradation and extends optimization to content-agnostic regions. To address these limitations, we propose PALU (Prefix-Aware Localized Unlearning), a framework driven by a local entropy maximization objective across both temporal and vocabulary dimensions. PALU reveals that (i) suppressing the sensitive prefix alone is sufficient to sever the causal generation link, and (ii) flattening only the top-$k$ logits is adequate to maximize uncertainty in the critical subspace. These findings allow PALU to alleviate redundant optimization across the full vocabulary and parameter space while minimizing collateral damage to general model performance. Comprehensive evaluations validate that PALU achieves superior forgetting efficacy and utility preservation compared to state-of-the-art baselines. Our code is available at https://github.com/nxZhai/PALU.
Abstract:Machine unlearning aims to forget sensitive knowledge from Large Language Models (LLMs) while maintaining general utility. However, existing approaches typically treat all tokens in a response indiscriminately and enforce uncertainty over the entire vocabulary. This global treatment results in unnecessary utility degradation and extends optimization to content-agnostic regions. To address these limitations, we propose PALU (Prefix-Aware Localized Unlearning), a framework driven by a local entropy maximization objective across both temporal and vocabulary dimensions. PALU reveals that (i) suppressing the sensitive prefix alone is sufficient to sever the causal generation link, and (ii) flattening only the top-$k$ logits is adequate to maximize uncertainty in the critical subspace. These findings allow PALU to alleviate redundant optimization across the full vocabulary and parameter space while minimizing collateral damage to general model performance. Comprehensive evaluations validate that PALU achieves superior forgetting efficacy and utility preservation compared to state-of-the-art baselines. Our code is available at this https URL.
Computation and Language (cs.CL)
arXiv:2601.03190 [cs.CL]
(or arXiv:2601.03190v4 [cs.CL] for this version)
Full text under the publisher's open licence · 165 words
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