  Interpretation methods to reveal the internal reasoning processes behind
machine learning models have attracted increasing attention in recent years. To
quantify the extent to which the identified interpretations truly reflect the
intrinsic decision-making mechanisms, various faithfulness evaluation metrics
have been proposed. However, we find that different faithfulness metrics show
conflicting preferences when comparing different interpretations. Motivated by
this observation, we aim to conduct a comprehensive and comparative study of
the widely adopted faithfulness metrics. In particular, we introduce two
assessment dimensions, namely diagnosticity and time complexity. Diagnosticity
refers to the degree to which the faithfulness metric favours relatively
faithful interpretations over randomly generated ones, and time complexity is
measured by the average number of model forward passes. According to the
experimental results, we find that sufficiency and comprehensiveness metrics
have higher diagnosticity and lower time complexity than the other faithfulness
metric

Computation and Language;Machine Learning