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Most benchmark datasets targeting commonsense reasoning focus on everydayscenarios: physical knowledge like knowing that you could fill a cup under awaterfall [Talmor et al., 2019], social knowledge like bumping into someone isawkward [Sap et al., 2019], and other generic situations. However, there is arich space of commonsense inferences anchored to knowledge about specificentities: for example, deciding the truthfulness of a claim "Harry Potter canteach classes on how to fly on a broomstick." Can models learn to combineentity knowledge with commonsense reasoning in this fashion? We introduceCREAK, a testbed for commonsense reasoning about entity knowledge, bridgingfact-checking about entities (Harry Potter is a wizard and is skilled at ridinga broomstick) with commonsense inferences (if you're good at a skill you canteach others how to do it). Our dataset consists of 13k human-authored Englishclaims about entities that are either true or false, in addition to a smallcontrast set. Crowdworkers can easily come up with these statements and humanperformance on the dataset is high (high 90s); we argue that models should beable to blend entity knowledge and commonsense reasoning to do well here. Inour experiments, we focus on the closed-book setting and observe that abaseline model finetuned on existing fact verification benchmark struggles onCREAK. Training a model on CREAK improves accuracy by a substantial margin, butstill falls short of human performance. Our benchmark provides a unique probeinto natural language understanding models, testing both its ability toretrieve facts (e.g., who teaches at the University of Chicago?) and unstatedcommonsense knowledge (e.g., butlers do not yell at guests).

CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge