Explanations of neural models aim to reveal a model's decision-making process for its predictions. However, recent work shows that current methods giving explanations such as saliency maps or counterfactuals can be misleading, as they are prone to present reasons that are unfaithful to the model's inner workings. This work explores the challenging question of evaluating the faithfulness of natural language explanations (NLEs). To this end, we present two tests. First, we propose a counterfactual input editor for inserting reasons that lead to counterfactual predictions but are not reflected by the NLEs. Second, we reconstruct inputs from the reasons stated in the generated NLEs and check how often they lead to the same predictions. Our tests can evaluate emerging NLE models, proving a fundamental tool in the development of faithful NLEs.

翻译：神经模型解释旨在揭示模型对其预测的决策过程。然而，近期研究表明，当前提供的解释方法（如显著性图或反事实解释）可能具有误导性，因为它们容易呈现与模型内部机制不忠实的理由。本研究探讨评估自然语言解释（NLEs）忠实性这一具有挑战性的问题。为此，我们提出两项测试。首先，我们构建一个反事实输入编辑器，用于插入导致反事实预测但未被NLEs反映的原因。其次，我们从生成的NLEs所述原因中重构输入，并检验这些重构输入在多大程度上产生相同预测。本测试可评估新兴NLE模型，为开发忠实NLEs提供基础工具。