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.
@article{arxiv.2210.07993,
title = {MiQA: A Benchmark for Inference on Metaphorical Questions},
author = {Iulia-Maria Comsa and Julian Martin Eisenschlos and Srini Narayanan},
journal= {arXiv preprint arXiv:2210.07993},
year = {2022}
}