LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers propose LACUNA, a benchmark to evaluate localization precision in LLM unlearning, addressing gaps in current methods that may only obfuscate rather than erase sensitive knowledge.
LLMs memorize sensitive training data, including personally identifiable information (PII), creating a pressing need for reliable post hoc removal methods. Unlearning has emerged as a promising solution, with state-of-the-art(SOTA) methods often following a localize-first, unlearn-second paradigm that targets specific model parameters. However, existing benchmarks evaluate unlearning solely at the output level, leaving open the question of whether unlearning truly erases knowledge from a model's parameters or merely obfuscates it, a concern reinforced by the success of resurfacing attacks. To
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