Incorporating Attribution Importance for Improving Faithfulness Metrics (2023)

Abstract: 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.

## introduction feature attribution methods (fas) are popular posthoc explanation methods that are applied after model training to assign an importance score to each token in the input @xcite . these scores indicate how much each token contributes to the model prediction. typically, the top-k ranked tokens are then selected to form an explanation, i.e. rationale @xcite . however, it is an important challenge to choose a fa for a natural language processing (nlp) task at hand @xcite since there is no sin-1 our code: https://github.com/casszhao/so ftfaith gle fa that is consistently more faithful @xcite . to assess whether a rationale extracted with a given fa is faithful, i.e. actually reflects the true model reasoning @xcite , various faithfulness metrics have been proposed @xcite @xcite . @xcite , also referred to as fidelity metrics @xcite , are two widely used metrics which have been found to be effective in capturing rationale faithfulness @xcite . both metrics use a hard erasure criterion for perturbing the input by entirely removing (i.e. comprehensiveness) or retaining (i.e. sufficiency) the rationale to observe changes in predictive likelihood. however, the hard erasure criterion ignores the different importance of each individual token, treating them all equally for computing sufficiency and comprehensiveness. moreover, the hard-perturbed input is likely to fall out of the distribution the model is trained on, leading to inaccurate measurements of faithfulness @xcite @xcite . figure 1 shows an example of two toy fas, a and b, identifying the same top two tokens ("like", "movie") as a rationale for the prediction. still, each of them assigns different importance scores to the two tokens resulting into different rankings. according to the hard erasure criterion, comprehensiveness and sufficiency will assign the same faithfulness score to the two rationales extracted by the two fas. in this paper, we aim to improve sufficiency and comprehensiveness in capturing the faithfulness of a fa. we achieve this by replacing the hard token perturbation with a simple yet effective soft erasure criterion (see figure 1 for an intuitive example). instead of entirely removing or retaining tokens from the input, we randomly mask parts of token vector representations proportionately to their fa importance. our main contributions are as follows: ## impact of rationale length on faithfulness and diagnosticity up to this point, we have only considered computing cumulative aopc nc and ns by evaluat-ing faithfulness scores at multiple rationale lengths together (see section 3). here, we explore how faithfulness and diagnosticity of nc and ns at individual rationale lengths compare to soft-nc and soft-ns. we note that both 'soft' metrics do not take the rationale length into account. ## conclusion in this work, we have proposed a new softperturbation approach for evaluating the faithfulness of input token importance assigned by fas. instead of perturbing the input by entirely removing or retaining tokens for measuring faithfulness, we incorporate the attribution importance by ran-domly masking parts of the token embeddings. our soft-sufficiency and soft-comprehensiveness metrics are consistently more effective in capturing more faithful fas across various nlp tasks. in the future, we plan to experiment with sequence labeling tasks. exploring differences in faithfulness metrics across different languages is also an interesting avenue for future work.