A common approach to explain NLP models, is to use importance measures that
express which tokens are important for a prediction. Unfortunately, such
explanations are often wrong despite being persuasive. Therefore, it is
essential to measure their faithfulness. One such metric is if tokens are truly
important, then masking them should result in worse model performance. However,
token masking introduces out-of-distribution issues and existing solutions are
computationally expensive and employ proxy-models. Furthermore, other metrics
are very limited in scope. In this work, we propose an inherently faithfulness
measurable model that addresses these challenges. This is achieved by using a
novel fine-tuning method that incorporates masking, such that masking tokens
become in-distribution by design. This differs from existing approaches, which
are completely model-agnostic but are inapplicable in practice. We demonstrate
the generality of our approach by applying it to various tasks and validate it
using statistical in-distribution tests. Additionally, because masking is
in-distribution, importance measures which themselves use masking become more
faithful, thus our model becomes more explainable.

提议基于掩码方法的一种内在的可靠度可衡量模型，该模型能够解决现有方法受限、计算昂贵且基于代理模型的问题，通过在设计上使掩码令牌成为数据分布内的方式，从而能够更有效地解释和说明 NLP 模型。