用梯度指导选择遗忘与保留数据，提升大模型删减无效行为的效率。
GRACE:Gradient-guided Coreset Selection for LLM Unlearning
- 通过种子样本梯度确定遗忘方向，用非负正交匹配法选精简遗忘集。
- 在保留集上投影消除遗忘方向，再分簇匹配提升模型性能。
- 适用于需动态删减不良行为的大模型场景，比现有方法更稳定高效。
大语言模型的机器遗忘方法通常假设有预先指定的遗忘和保留数据集。但在真实场景中，用户请求可能仅提供少量不当行为示例，需从异构语料中推断出遗忘和保留集。本文研究此数据选择问题，提出GRACE——一种基于梯度引导的核心集选择方法，用于构建大模型遗忘中的遗忘与保留集。GRACE首先从诱发不当行为的种子样本中计算遗忘方向，再利用非负正交匹配追踪法选取能近似该方向的紧凑遗忘核心集。为保持模型能力，它在投影掉遗忘方向后，于剩余梯度空间中采用分簇正交匹配追踪法选取保留样本。在两个目标领域、两种模型架构及四种遗忘算法下，GRACE在保持相当遗忘效果的同时显著提升模型性能，尤其在各类基于梯度的选择方法中表现更一致可靠。
原文摘要 · Abstract (English)
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 retain sets to be inferred from heterogeneous corpora. We study this data-selection problem and propose GRACE , a gradient-guided coreset selection method that constructs both forget and retain sets for LLM unlearning. GRACE first computes a forget direction from seed examples that elicit the undesired behavior, then selects a compact forget coreset whose gradients approximate this direction using non-negative orthogonal matching pursuit. To preserve model utility, it selects retain examples after projecting out the forget direction and applying clustered orthogonal matching pursuit in the remaining gradient space. Across two target domains, two model families, and four unlearning algorithms, GRACE improves model utility while maintaining comparable forget quality, with particularly consistent gains over prior gradient-based selection methods.
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