  Generics express generalizations about the world (e.g., birds can fly) that
are not universally true (e.g., newborn birds and penguins cannot fly).
Commonsense knowledge bases, used extensively in NLP, encode some generic
knowledge but rarely enumerate such exceptions and knowing when a generic
statement holds or does not hold true is crucial for developing a comprehensive
understanding of generics. We present a novel framework informed by linguistic
theory to generate exemplars -- specific cases when a generic holds true or
false. We generate ~19k exemplars for ~650 generics and show that our framework
outperforms a strong GPT-3 baseline by 12.8 precision points. Our analysis
highlights the importance of linguistic theory-based controllability for
generating exemplars, the insufficiency of knowledge bases as a source of
exemplars, and the challenges exemplars pose for the task of natural language
inference.
