In Natural Language Processing, feature-additive explanation methods quantify the independent contribution of each input token towards a model's decision. By computing the rank correlation between attention weights and the scores produced by a small sample of these methods, previous analyses have sought to either invalidate or support the role of attention-based explanations as a faithful and plausible measure of salience. To investigate what measures of rank correlation can reliably conclude, we comprehensively compare feature-additive methods, including attention-based explanations, across several neural architectures and tasks. In most cases, we find that none of our chosen methods agree. Therefore, we argue that rank correlation is largely uninformative and does not measure the quality of feature-additive methods. Additionally, the range of conclusions a practitioner may draw from a single explainability algorithm are limited.

研究表明，基于排名相关性进行的注意力权重和特征添加解释方法之间的相关性分析，要么证明了基于注意力的解释作为显著性的忠实且合理的措施的作用，要么否定了这一点。但是，作者对LIME，DeepLIFT等多种解释技术的实验结果表明，并不存在一个能够弥合这些技术之间存在的明显分歧的排名相关度量方法。因此，作者建议从社区提出的严谨诊断方法入手，进行模型解释的改进。

法庭秩序：易发生分歧的可解释人工智能方法