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We propose a benchmark to assess the capability of large language models toreason with conventional metaphors. Our benchmark combines the previouslyisolated topics of metaphor detection and commonsense reasoning into a singletask that requires a model to make inferences by accurately selecting betweenthe literal and metaphorical register. We examine the performance ofstate-of-the-art pre-trained models on binary-choice tasks and find a largediscrepancy between the performance of small and very large models, going fromchance to near-human level. We also analyse the largest model in a generativesetting and find that although human performance is approached, carefulmultiple-shot prompting is required.

MiQA: A Benchmark for Inference on Metaphorical Questions