MiQA: A Benchmark for Inference on Metaphorical Questions

Iulia Comșa, Julian Eisenschlos, Srini Narayanan


Abstract
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.
Anthology ID:
2022.aacl-short.46
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2022
Address:
Online only
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
373–381
Language:
URL:
https://aclanthology.org/2022.aacl-short.46
DOI:
Bibkey:
Cite (ACL):
Iulia Comșa, Julian Eisenschlos, and Srini Narayanan. 2022. MiQA: A Benchmark for Inference on Metaphorical Questions. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 373–381, Online only. Association for Computational Linguistics.
Cite (Informal):
MiQA: A Benchmark for Inference on Metaphorical Questions (Comșa et al., AACL-IJCNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.aacl-short.46.pdf
Dataset:
 2022.aacl-short.46.Dataset.tsv