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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.aacl-short.46.pdf