可测量忠实度的掩码语言模型
Faithfulness Measurable Masked Language Models
AI总结：
本文提出一种结合掩码的新型微调方法，使掩码token成为分布内数据，从而构建出可内在测量忠实度的模型，在16个数据集上验证了其通用性，并间接实现了内在可解释性。
摘要：
解释NLP模型的一种常见方法是使用重要性度量，表达哪些token对预测是重要的。不幸的是，这种解释尽管具有说服力，却往往是错误的。因此，衡量其忠实度至关重要。其中一个度量标准是：如果token确实重要，那么对它们进行掩码应该导致模型性能下降。然而，token掩码会引入分布外问题，而解决此问题的现有方法计算成本高昂且采用代理模型。此外，其他度量标准的适用范围非常有限。本研究提出了一种可内在测量忠实度的模型来解决这些挑战。这是通过一种结合掩码的新型微调方法实现的，使得掩码token在设计上成为分布内数据。这不同于现有的完全与模型无关但在实践中不可用的方法。我们通过将该方法应用于16个不同的数据集，并使用统计分布内测试进行验证，展示了其通用性。随后，使用9种不同的重要性度量来衡量忠实度。由于掩码是分布内的，本身使用掩码的重要性度量变得更加一致地忠实。此外，由于该模型使忠实度的测量成本降低，我们可以优化解释以实现最大忠实度；因此，我们的模型间接地具备了内在可解释性。
英文摘要：
A common approach to explaining 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 that address this are computationally expensive and employ proxy models. Furthermore, other metrics are very limited in scope. This work proposes an inherently faithfulness measurable model that addresses these challenges. This is achieved 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 16 different datasets and validate it using statistical in-distribution tests. The faithfulness is then measured with 9 different importance measures. Because masking is in-distribution, importance measures that themselves use masking become consistently more faithful. Additionally, because the model makes faithfulness cheap to measure, we can optimize explanations towards maximal faithfulness; thus, our model becomes indirectly inherently explainable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
