Instead of constructing importance scores over the input texts on which the model makes predictions, such methods rank training examples by their influence on the model's prediction for the test input (Caruana et al., 1999;Koh and Liang, 2017;Card et al., 2019).
we are interested in the use of influence functions (Koh and Liang, 2017), which are in a sense inherently 'faithful' in that they reveal the training examples most responsible for particular predictions.