Scientific and Creative Analogies in Pretrained Language Models
Tamara Czinczoll, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova · 2022
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-ofthe-art LMs achieve low performance on these complex analogy tasks, highlighting the challenges still posed by analogy understanding.