  Feature attribution methods (FAs) are popular approaches for providing
insights into the model reasoning process of making predictions. The more
faithful a FA is, the more accurately it reflects which parts of the input are
more important for the prediction. Widely used faithfulness metrics, such as
sufficiency and comprehensiveness use a hard erasure criterion, i.e. entirely
removing or retaining the top most important tokens ranked by a given FA and
observing the changes in predictive likelihood. However, this hard criterion
ignores the importance of each individual token, treating them all equally for
computing sufficiency and comprehensiveness. In this paper, we propose a simple
yet effective soft erasure criterion. Instead of entirely removing or retaining
tokens from the input, we randomly mask parts of the token vector
representations proportionately to their FA importance. Extensive experiments
across various natural language processing tasks and different FAs show that
our soft-sufficiency and soft-comprehensiveness metrics consistently prefer
more faithful explanations compared to hard sufficiency and comprehensiveness.
Our code: https://github.com/casszhao/SoftFaith
