Scientific and Creative Analogies in Pretrained Language Models
Abstract
This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogical relations, with a high similarity of the two domains between which the analogy holds. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy dataset containing systematic mappings of multiple attributes and relational structures across dissimilar domains. Using this dataset, we test the analogical reasoning capabilities of several widely-used pretrained language models (LMs). We find that state-of-the-art LMs achieve low performance on these complex analogy tasks, highlighting the challenges still posed by analogy understanding.
Cite
@article{arxiv.2211.15268,
title = {Scientific and Creative Analogies in Pretrained Language Models},
author = {Tamara Czinczoll and Helen Yannakoudakis and Pushkar Mishra and Ekaterina Shutova},
journal= {arXiv preprint arXiv:2211.15268},
year = {2022}
}
Comments
To be published in Findings of EMNLP 2022