Training data attribution (TDA) techniques find influential training data for
the model's prediction on the test data of interest. They approximate the
impact of down- or up-weighting a particular training sample. While
conceptually useful, they are hardly applicable in practice, particularly
because of their sensitivity to different model initialisation. In this paper,
we introduce a Bayesian perspective on the TDA task, where the learned model is
treated as a Bayesian posterior and the TDA estimates as random variables. From
this novel viewpoint, we observe that the influence of an individual training
sample is often overshadowed by the noise stemming from model initialisation
and SGD batch composition. Based on this observation, we argue that TDA can
only be reliably used for explaining model predictions that are consistently
influenced by certain training data, independent of other noise factors. Our
experiments demonstrate the rarity of such noise-independent training-test data
pairs but confirm their existence. We recommend that future researchers and
practitioners trust TDA estimates only in such cases. Further, we find a
disagreement between ground truth and estimated TDA distributions and encourage
future work to study this gap. Code is provided at
this https URL

本文研究了训练数据归因技术，并提出了基于贝叶斯视角的训练数据归因方法；作者观察到单个训练样本的影响往往被模型初始化和 SGD 批量组成等噪音所掩盖，只有在某些特殊情况下才能可靠地使用训练数据归因来解释模型预测结果。