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        <title>Testing the limits of logical reasoning in neural and hybrid models</title>
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        <namePart type="given">Manuel</namePart>
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        <namePart type="given">Maciej</namePart>
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            <namePart type="given">Kevin</namePart>
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        <name type="personal">
            <namePart type="given">Helena</namePart>
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    <abstract>We study the ability of neural and hybrid models to generalize logical reasoning patterns. We created a series of tests for analyzing various aspects of generalization in the context of language and reasoning, focusing on compositionality and recursiveness. We used them to study the syllogistic logic in hybrid models, where the network assists in premise selection. We analyzed feed-forward, recurrent, convolutional, and transformer architectures. Our experiments demonstrate that even though the models can capture elementary aspects of the meaning of logical terms, they learn to generalize logical reasoning only to a limited degree.</abstract>
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%0 Conference Proceedings
%T Testing the limits of logical reasoning in neural and hybrid models
%A Guzman, Manuel
%A Szymanik, Jakub
%A Malicki, Maciej
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F guzman-etal-2024-testing
%X We study the ability of neural and hybrid models to generalize logical reasoning patterns. We created a series of tests for analyzing various aspects of generalization in the context of language and reasoning, focusing on compositionality and recursiveness. We used them to study the syllogistic logic in hybrid models, where the network assists in premise selection. We analyzed feed-forward, recurrent, convolutional, and transformer architectures. Our experiments demonstrate that even though the models can capture elementary aspects of the meaning of logical terms, they learn to generalize logical reasoning only to a limited degree.
%U https://aclanthology.org/2024.findings-naacl.147
%P 2267-2279

Markdown (Informal)

[Testing the limits of logical reasoning in neural and hybrid models](https://aclanthology.org/2024.findings-naacl.147) (Guzman et al., Findings 2024)

• Testing the limits of logical reasoning in neural and hybrid models (Guzman et al., Findings 2024)

ACL

• Manuel Guzman, Jakub Szymanik, and Maciej Malicki. 2024. Testing the limits of logical reasoning in neural and hybrid models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2267–2279, Mexico City, Mexico. Association for Computational Linguistics.