鲁棒的不可靠性：当掩码语言模型上的忠实度度量具有误导性时
Robust Infidelity: When Faithfulness Measures on Masked Language Models Are Misleading
AI总结：
本文指出基于迭代掩码计算忠实度指标来比较神经文本分类器可解释性存在陷阱，该属性更适合描述为“对迭代掩码的敏感性”，并发现其会导致分布外嵌入和不可预测行为，从而为解释该指标提供指导。
摘要：
量化神经文本分类器可解释性的一种常见方法是，基于迭代掩码显著输入词元并测量模型预测的变化来计算忠实度指标。我们提出，该属性更适合描述为“对迭代掩码的敏感性”，并强调了使用该指标比较文本分类器可解释性时的陷阱。我们表明，迭代掩码在其他方面可比的 Transformer 编码器文本分类器之间产生了忠实度分数的巨大差异。然后我们证明，迭代掩码样本产生的嵌入超出了训练期间所见分布，导致不可预测的行为。我们进一步探讨了破坏使用迭代掩码进行可解释性原则性比较的任务特定考量，例如与基于显著性的对抗攻击的潜在相似性。我们的发现深入探讨了这些行为如何影响神经文本分类器，并就如何解释对迭代掩码的敏感性提供了指导。
英文摘要：
A common approach to quantifying neural text classifier interpretability is to calculate faithfulness metrics based on iteratively masking salient input tokens and measuring changes in the model prediction. We propose that this property is better described as "sensitivity to iterative masking", and highlight pitfalls in using this measure for comparing text classifier interpretability. We show that iterative masking produces large variation in faithfulness scores between otherwise comparable Transformer encoder text classifiers. We then demonstrate that iteratively masked samples produce embeddings outside the distribution seen during training, resulting in unpredictable behaviour. We further explore task-specific considerations that undermine principled comparison of interpretability using iterative masking, such as an underlying similarity to salience-based adversarial attacks. Our findings give insight into how these behaviours affect neural text classifiers, and provide guidance on how sensitivity to iterative masking should be interpreted.
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。
