GRACE解决了LLM遗忘中遗忘集和保留集推断问题，通过梯度引导方法提升模型效用，比现有梯度选择方法更稳定。
GRACE是一种针对大语言模型遗忘的数据选择方法，通过种子样本计算遗忘方向，使用非负正交匹配选择紧凑遗忘集。该方法在两个目标领域、两个模型家族和四种遗忘算法上测试，在保持可比遗忘质量的同时提升了模型效用。GRACE在梯度引导的选择方法上展现出一致的改进效果。
GRACE:Gradient-guided Coreset Selection for LLM Unlearning
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.