Abstract
In this study, we create a CConS (Counter-commonsense Contextual Size comparison) dataset to investigate how physical commonsense affects the contextualized size comparison task; the proposed dataset consists of both contexts that fit physical commonsense and those that do not. This dataset tests the ability of language models to predict the size relationship between objects under various contexts generated from our curated noun list and templates. We measure the ability of several masked language models and encoder-decoder models. The results show that while large language models can use prepositions such as “in” and “into” in the provided context to infer size relationships, they fail to use verbs and thus make incorrect judgments led by their prior physical commonsense.- Anthology ID:
- 2023.acl-short.53
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 603–612
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.53
- DOI:
- 10.18653/v1/2023.acl-short.53
- Cite (ACL):
- Kazushi Kondo, Saku Sugawara, and Akiko Aizawa. 2023. Probing Physical Reasoning with Counter-Commonsense Context. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 603–612, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Probing Physical Reasoning with Counter-Commonsense Context (Kondo et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.acl-short.53.pdf