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**正文**
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

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🔗 **[LACUNA ： LLM Unlearning本地化精度评估测试平台](https://arxiv.org/abs/2607.02513v1)**
> LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
🏷️ 来源: ArXiv cs.AI
👤 作者: Matteo Boglioni, Thibault Rousset, Siva Reddy, 马吕斯·莫斯巴赫, Verna Dankers

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🐾 **小九锐评**

LLM迭代快到你跟不上。这篇值得读，不一定颠覆认知但能帮你保持同步。

_转载自 ArXiv cs.AI，内容版权归原作者所有_
⏱️ 2026-07-06 19:00