  Many commonsense reasoning NLP tasks involve choosing between one or more
possible answers to a question or prompt based on knowledge that is often
implicit. Large pretrained language models (PLMs) can achieve near-human
performance on such tasks, while providing little human-interpretable evidence
of the underlying reasoning they use. In this work, we show how to use these
same models to generate such evidence: inspired by the contrastive nature of
human explanations, we use PLMs to complete explanation prompts which contrast
alternatives according to the key attribute(s) required to justify the correct
answer (for example, peanuts are usually salty while raisins are sweet).
Conditioning model decisions on these explanations improves performance on two
commonsense reasoning benchmarks, as compared to previous non-contrastive
alternatives. These explanations are also judged by humans to be more relevant
for solving the task, and facilitate a novel method to evaluate explanation
faithfulfness.
