End-to-end neural Natural Language Processing (NLP) models are notoriously
difficult to understand. This has given rise to numerous efforts towards model
explainability in recent years. One desideratum of model explanation is
faithfulness, i.e. an explanation should accurately represent the reasoning
process behind the model's prediction. In this survey, we review over 110 model
explanation methods in NLP through the lens of faithfulness. We first discuss
the definition and evaluation of faithfulness, as well as its significance for
explainability. We then introduce recent advances in faithful explanation,
grouping existing approaches into five categories: similarity methods, analysis
of model-internal structures, backpropagation-based methods, counterfactual
intervention, and self-explanatory models. For each category, we synthesize its
representative studies, strengths, and weaknesses. Finally, we summarize their
common virtues and remaining challenges, and reflect on future work directions
towards faithful explainability in NLP.

本综述通过对忠实度的镜头，回顾了超过 110 种自然语言处理中的模型解释方法。我们考虑了忠实度的定义和评估，以及它对可解释性的重要性，并将现有方法分为五类。最后，我们总结了它们的共同优点和未解决的挑战，并反思了未来的研究方向。