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

Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions