Abstract

As a core cognitive skill that enables the transferability of information across domains, analogical reasoning has been extensively studied for both humans and computational models. However, while cognitive theories of analogy often focus on narratives and study the distinction between surface, relational, and system similarities, existing work in natural language processing has a narrower focus as far as relational analogies between word pairs. This gap brings a natural question: can state-of-the-art large language models (LLMs) detect system analogies between narratives? To gain insight into this question and extend word-based relational analogies to relational system analogies, we devise a comprehensive computational framework that operationalizes dominant theories of analogy, using narrative elements to create surface and system mappings. Leveraging the interplay between these mappings, we …

Metadata

publication

arXiv preprint arXiv:2310.00996, 2024

year

2024

publication date

2024/4

authors

Zhivar Sourati, Filip Ilievski, Pia Sommerauer, Yifan Jiang

link

https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00688/124260

resource_link

https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00688/124260

journal

arXiv preprint arXiv:2310.00996