Perturbation-based explanation methods such as LIME and SHAP are commonly
applied to text classification. This work focuses on their extension to
generative language models. To address the challenges of text as output and
long text inputs, we propose a general framework called MExGen that can be
instantiated with different attribution algorithms. To handle text output, we
introduce the notion of scalarizers for mapping text to real numbers and
investigate multiple possibilities. To handle long inputs, we take a
multi-level approach, proceeding from coarser levels of granularity to finer
ones, and focus on algorithms with linear scaling in model queries. We conduct
a systematic evaluation, both automated and human, of perturbation-based
attribution methods for summarization and context-grounded question answering.
The results show that our framework can provide more locally faithful
explanations of generated outputs.

我们提出了一个名为 MExGen 的通用框架，可以扩展文本分类中的扰动解释方法（如 LIME 和 SHAP）以应对生成语言模型的挑战，该框架可用于不同的归因算法，并通过标量化器将文本映射到实数处理文本输出，同时采用多层级方法处理长输入，通过从粗粒度到细粒度的方式专注于具有模型查询线性扩展的算法，并进行系统评估，结果表明我们的框架能够提供更本地准确的生成输出解释。