  Large language models have demonstrated impressive performance on commonsense
tasks; however, these tasks are often posed as multiple-choice questions,
allowing models to exploit systematic biases. Commonsense is also inherently
probabilistic with multiple correct answers. The purpose of "boiling water"
could be making tea and cooking, but it also could be killing germs. Existing
tasks do not capture the probabilistic nature of common sense. To this end, we
present commonsense frame completion (CFC), a new generative task that
evaluates common sense via multiple open-ended generations. We also propose a
method of probabilistic evaluation that strongly correlates with human
judgments. Humans drastically outperform strong language model baselines on our
dataset, indicating this approach is both a challenging and useful evaluation
of machine common sense.
