  We propose a benchmark to assess the capability of large language models to
reason with conventional metaphors. 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 pre-trained models on binary-choice tasks and find a large
discrepancy between the performance of small and very large models, going from
chance to near-human level. We also analyse the largest model in a generative
setting and find that although human performance is approached, careful
multiple-shot prompting is required.
