Thus, when a model recognizes a highly indicative pattern of the predicted class k, the tokens involved in the pattern would have highly positive saliency scores for this class and highly negative saliency scores for the remaining classes.
when the model is not highly confident, we can assume that it is unable to recognize a strong indication of any class, and the tokens accordingly do not have high saliency scores for any class.