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arXiv · 2211.15268

Scientific and Creative Analogies in Pretrained Language Models

Tamara Czinczoll·Helen Yannakoudakis·Pushkar Mishra·Ekaterina Shutova
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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.

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Tamara Czinczoll, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova. 2022-11-28. Scientific and Creative Analogies in Pretrained Language Models. https://arxiv.org/abs/2211.15268

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