The empirical risk function is the cornerstone of machine learning.

While the annotation cost (i.e., BLIP2 caption and CLIP score generation) is 6

Firstly, many existing attribution techniques require either computing the gradients [58] or multiple queries to the model [19], both of which are slow and computationally expensive. In contrast, ICL is often applied inreal-time to a large foundation model [12] that necessitates the attribution approaches for ICL to be fast and efficient.

Additionally, we provide the calibration performance of various competitive approaches. Briefly, calibration quantifies how similar a model's confidence and its accuracy are [Osborne, 1991]). To measure it, we employ the recently proposed Adaptive ECE (AdaECE) [Mukhoti et al., 2020]. For all the methods, the AdaECE is computed after performing temperature scaling [Guoetal.,2017] Unfortunately, we could not manage to make their code work on C100 as the training procedure seemed to be unstable.

Asstated in Section 2, most GNNs can be interpreted as performing message passing on node features, followed by feature transformation and an activation function (Eq.