We propose a benchmark to assess the capability of large language models to reason with metaphor. Our benchmark combines the previously isolated topics of metaphor detection and commonsense reasoning into a single task that requires a model to make inferences by accurately selecting between the literal and metaphorical register. We examine the performance of state-of-the-art pretrained models on forced-choice tasks and find a large discrepancy between small and very large models, going from chance- to human-level performance. However, upon examining the generative performance of the largest model, we find that there is still a gap to bridge before human performance is reached in a more natural conversational setting.
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