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Deep learning systems frequently fail at out-of-context (OOC) prediction, theproblem of making reliable predictions on uncommon or unusual inputs orsubgroups of the training distribution. To this end, a number of benchmarks formeasuring OOC performance have recently been introduced. In this work, weintroduce a framework unifying the literature on OOC performance measurement,and demonstrate how rich auxiliary information can be leveraged to identifycandidate sets of OOC examples in existing datasets. We present NOOCh: a suiteof naturally-occurring "challenge sets", and show how varying notions ofcontext can be used to probe specific OOC failure modes. Experimentally, weexplore the tradeoffs between various learning approaches on these challengesets and demonstrate how the choices made in designing OOC benchmarks can yieldvarying conclusions.

Identifying and Benchmarking Natural Out-of-Context Prediction Problems