The proliferation of large-scale AI models trained on extensive datasets has
revolutionized machine learning. With these models taking on increasingly
central roles in various applications, the need to understand their behavior
and enhance interpretability has become paramount. To investigate the impact of
changes in training data on a pre-trained model, a common approach is
leave-one-out retraining. This entails systematically altering the training
dataset by removing specific samples to observe resulting changes within the
model. However, retraining the model for each altered dataset presents a
significant computational challenge, given the need to perform this operation
for every dataset variation. In this paper, we introduce an efficient framework
for assessing data impact, comprising offline training and online evaluation
stages. During the offline training phase, we approximate the influence of
training data on the target model through a distilled synset, formulated as a
reversed gradient matching problem. For online evaluation, we expedite the
leave-one-out process using the synset, which is then utilized to compute the
attribution matrix based on the evaluation objective. Experimental evaluations,
including training data attribution and assessments of data quality,
demonstrate that our proposed method achieves comparable model behavior
evaluation while significantly speeding up the process compared to the direct
retraining method.

通过在线和离线阶段，我们提出了一种高效的框架，评估训练数据对目标模型的影响，通过反向梯度匹配问题建立了一个精简同义词集，用于加速离开一个样本的过程，并计算评估目标基于的归因矩阵。实验证明，相比直接重新训练方法，我们的方法在实现了可比较的模型行为评估的同时，显著加快了过程。