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        <title>ARN: Analogical Reasoning on Narratives</title>
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        <namePart type="given">Zhivar</namePart>
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        <namePart type="given">Filip</namePart>
        <namePart type="family">Ilievski</namePart>
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    <name type="personal">
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    <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 create a binary task and benchmark for Analogical Reasoning on Narratives (ARN), covering four categories of far (cross-domain)/near (within-domain) analogies and disanalogies. We show that while all LLMs can largely recognize near analogies, even the largest ones struggle with far analogies in a zero-shot setting, with GPT4.0 scoring below random. Guiding the models through solved examples and Chain-of-Thought reasoning enhances their analogical reasoning ability. Yet, since even in the few-shot setting, the best model only performs halfway between random and humans, ARN opens exciting directions for computational analogical reasoners.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00688</identifier>
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        <date>2024</date>
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            <start>1063</start>
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%0 Journal Article
%T ARN: Analogical Reasoning on Narratives
%A Sourati, Zhivar
%A Ilievski, Filip
%A Sommerauer, Pia
%A Jiang, Yifan
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F sourati-etal-2024-arn
%X 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 create a binary task and benchmark for Analogical Reasoning on Narratives (ARN), covering four categories of far (cross-domain)/near (within-domain) analogies and disanalogies. We show that while all LLMs can largely recognize near analogies, even the largest ones struggle with far analogies in a zero-shot setting, with GPT4.0 scoring below random. Guiding the models through solved examples and Chain-of-Thought reasoning enhances their analogical reasoning ability. Yet, since even in the few-shot setting, the best model only performs halfway between random and humans, ARN opens exciting directions for computational analogical reasoners.
%R 10.1162/tacl_a_00688
%U https://aclanthology.org/2024.tacl-1.59/
%U https://doi.org/10.1162/tacl_a_00688
%P 1063-1086

Markdown (Informal)

[ARN: Analogical Reasoning on Narratives](https://aclanthology.org/2024.tacl-1.59/) (Sourati et al., TACL 2024)

• ARN: Analogical Reasoning on Narratives (Sourati et al., TACL 2024)

ACL

• Zhivar Sourati, Filip Ilievski, Pia Sommerauer, and Yifan Jiang. 2024. ARN: Analogical Reasoning on Narratives. Transactions of the Association for Computational Linguistics, 12:1063–1086.