Generating Interpretable Rationales for Open-Book Question Answering Using Markup-and-Mask Techniques

The authors propose a new style of rationale for open-book question answering, called markup-and-mask, which combines aspects of extractive and free-text explanations. They leverage in-context learning with a pretrained language model to generate silver annotated data for training an "honest student" model that produces these rationales without explicit supervision.