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        <title>Scientific and Creative Analogies in Pretrained Language Models</title>
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    <name type="personal">
        <namePart type="given">Tamara</namePart>
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        <namePart type="given">Helen</namePart>
        <namePart type="family">Yannakoudakis</namePart>
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        <namePart type="given">Pushkar</namePart>
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    <name type="personal">
        <namePart type="given">Ekaterina</namePart>
        <namePart type="family">Shutova</namePart>
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            <namePart type="given">Yoav</namePart>
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            <namePart type="given">Yue</namePart>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.153</identifier>
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        <date>2022-12</date>
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            <start>2094</start>
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%0 Conference Proceedings
%T Scientific and Creative Analogies in Pretrained Language Models
%A Czinczoll, Tamara
%A Yannakoudakis, Helen
%A Mishra, Pushkar
%A Shutova, Ekaterina
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F czinczoll-etal-2022-scientific
%X 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.
%R 10.18653/v1/2022.findings-emnlp.153
%U https://aclanthology.org/2022.findings-emnlp.153
%U https://doi.org/10.18653/v1/2022.findings-emnlp.153
%P 2094-2100

Markdown (Informal)

[Scientific and Creative Analogies in Pretrained Language Models](https://aclanthology.org/2022.findings-emnlp.153) (Czinczoll et al., Findings 2022)

• Scientific and Creative Analogies in Pretrained Language Models (Czinczoll et al., Findings 2022)

ACL

• Tamara Czinczoll, Helen Yannakoudakis, Pushkar Mishra, and Ekaterina Shutova. 2022. Scientific and Creative Analogies in Pretrained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2094–2100, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.