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        <title>Hierarchical syntactic structure in human-like language models</title>
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        <namePart type="given">Michael</namePart>
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        <namePart type="given">Donald</namePart>
        <namePart type="family">Dunagan</namePart>
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        <namePart type="given">Jonathan</namePart>
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        <namePart type="family">Hale</namePart>
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            <title>Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics</title>
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            <namePart type="given">Tatsuki</namePart>
            <namePart type="family">Kuribayashi</namePart>
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            <namePart type="given">Giulia</namePart>
            <namePart type="family">Rambelli</namePart>
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            <namePart type="given">Ece</namePart>
            <namePart type="family">Takmaz</namePart>
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            <namePart type="given">Philipp</namePart>
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        <name type="personal">
            <namePart type="given">Yohei</namePart>
            <namePart type="family">Oseki</namePart>
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    <abstract>Language models (LMs) are a meeting point for cognitive modeling and computational linguistics. How should they be designed to serve as adequate cognitive models? To address this question, this study contrasts two Transformer-based LMs that share the same architecture. Only one of them analyzes sentences in terms of explicit hierarchical structure. Evaluating the two LMs against fMRI time series via the surprisal complexity metric, the results implicate the superior temporal gyrus. These findings underline the need for hierarchical sentence structures in word-by-word models of human language comprehension.</abstract>
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%0 Conference Proceedings
%T Hierarchical syntactic structure in human-like language models
%A Wolfman, Michael
%A Dunagan, Donald
%A Brennan, Jonathan
%A Hale, John
%Y Kuribayashi, Tatsuki
%Y Rambelli, Giulia
%Y Takmaz, Ece
%Y Wicke, Philipp
%Y Oseki, Yohei
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wolfman-etal-2024-hierarchical
%X Language models (LMs) are a meeting point for cognitive modeling and computational linguistics. How should they be designed to serve as adequate cognitive models? To address this question, this study contrasts two Transformer-based LMs that share the same architecture. Only one of them analyzes sentences in terms of explicit hierarchical structure. Evaluating the two LMs against fMRI time series via the surprisal complexity metric, the results implicate the superior temporal gyrus. These findings underline the need for hierarchical sentence structures in word-by-word models of human language comprehension.
%U https://aclanthology.org/2024.cmcl-1.6
%P 72-80

Markdown (Informal)

[Hierarchical syntactic structure in human-like language models](https://aclanthology.org/2024.cmcl-1.6) (Wolfman et al., CMCL-WS 2024)

• Hierarchical syntactic structure in human-like language models (Wolfman et al., CMCL-WS 2024)

ACL

• Michael Wolfman, Donald Dunagan, Jonathan Brennan, and John Hale. 2024. Hierarchical syntactic structure in human-like language models. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 72–80, Bangkok, Thailand. Association for Computational Linguistics.