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English(EN) GRACE:Gradient-guided Coreset Selection for LLM Unlearning

新的GRACE方法通过梯度引导的共核选择改进LLM遗忘

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了GRACE，一种新颖的LLM遗忘方法，解决了从有限示例中推断遗忘集和保留集所面临的挑战。GRACE利用梯度引导来构建这些集合，首先从种子示例中识别遗忘方向，然后选择一个紧凑的遗忘共核。为了保持模型效用，它通过投影出遗忘方向并采用聚类正交匹配追踪来选择保留示例。跨越不同领域和模型的实验表明，GRACE在保持遗忘质量的同时提高了模型效用，优于以前的基于梯度的选择技术。 AI

影响 这项研究提供了一种更有效、更可靠的LLM遗忘方法，有望改善数据隐私和模型控制。

排序理由 该集群包含一篇详细介绍LLM遗忘新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的GRACE方法通过梯度引导的共核选择改进LLM遗忘

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该集群包含一篇详细介绍LLM遗忘新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Praveen Bushipaka, Andrea D'Angelo, Lucia Passaro, Tommaso Cucinotta ·

    GRACE：用于大型语言模型遗忘的梯度引导核心集选择

    arXiv:2608.28361v1 Announce Type: cross Abstract: Machine Unlearning methods for Large Language Models typically assume pre-specified forget and retain sets. In realistic settings, however, requests may provide only a few examples of undesired behavior, requiring forget and retai…

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