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

在研究机器学习模型内部推理过程的解释方法越来越受到关注的今天，我们发现不同的忠诚度评估指标在比较不同的解释时显示出冲突的偏好，因此我们旨在对广泛采用的忠诚度指标进行全面而比较的研究，并引入两个评估维度：诊断性和时间复杂性，根据实验结果，我们发现足够性和综合性指标具有更高的诊断性和更低的时间复杂度。