Understanding the internal reasoning behind the predictions of machine
learning systems is increasingly vital, given their rising adoption and
acceptance. While previous approaches, such as LIME, generate algorithmic
explanations by attributing importance to input features for individual
examples, recent research indicates that practitioners prefer examining
language explanations that explain sub-groups of examples. In this paper, we
introduce MaNtLE, a model-agnostic natural language explainer that analyzes
multiple classifier predictions and generates faithful natural language
explanations of classifier rationale for structured classification tasks.
MaNtLE uses multi-task training on thousands of synthetic classification tasks
to generate faithful explanations. Simulated user studies indicate that, on
average, MaNtLE-generated explanations are at least 11% more faithful compared
to LIME and Anchors explanations across three tasks. Human evaluations
demonstrate that users can better predict model behavior using explanations
from MaNtLE compared to other techniques

本文介绍了 MaNtLE，一个模型无关的自然语言解释器，在结构化分类任务中分析多个分类器预测，并生成忠实的自然语言解释器，模拟的用户研究表明，MaNtLE 生成的解释器平均比 LIME 和 Anchors 解释器忠实度高至少 11％。