Feature attribution scores are used for explaining the prediction of a text
classifier to users by highlighting a k number of tokens. In this work, we
propose a way to determine the number of optimal k tokens that should be
displayed from sequential properties of the attribution scores. Our approach is
dynamic across sentences, method-agnostic, and deals with sentence length bias.
We compare agreement between multiple methods and humans on an NLI task, using
fixed k and dynamic k. We find that perturbation-based methods and Vanilla
Gradient exhibit highest agreement on most method--method and method--human
agreement metrics with a static k. Their advantage over other methods
disappears with dynamic ks which mainly improve Integrated Gradient and
GradientXInput. To our knowledge, this is the first evidence that sequential
properties of attribution scores are informative for consolidating attribution
signals for human interpretation.

从归属度量的序列特性来确认应该显示的最佳 k 个令牌数量。在 NLI 任务中，通过比较多种方法和人类之间的一致性，发现基于扰动的方法和 Vanilla Gradient 在静态 k 的大多数方法 - 方法和方法 - 人类一致性指标上具有最高的一致性，但随着动态 ks 的引入，他们相对于 Integrated Gradient 和 GradientXInput 的优势逐渐消失。据我们所知，这是归属度量序列特性在人类解释方面提供信息的首个证据。