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    <titleInfo>
        <title>Do Language Models Have Semantics? On the Five Standard Positions</title>
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        <namePart type="given">Anders</namePart>
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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
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        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Nabende</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Mohammad</namePart>
            <namePart type="given">Taher</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>We identify five positions on whether large language models (LLMs) and chatbots can be said to exhibit semantic understanding. These positions differ in whether they attribute semantics to LLMs and/or chatbots trained on feedback, what kind of semantics they attribute (inferential or referential), and in virtue of what they attribute referential semantics (internal or external causes). This allows for 2⌃⌃4=16 logically possible positions, but we have only seen people argue for five of these. Based on a pairwise comparison of these five positions, we conclude that the better theory of semantics in large language models is, in fact, a sixth combination: Both large language models and chatbots have inferential and referential semantics, grounded in both internal and external causes.</abstract>
    <identifier type="citekey">sogaard-2025-language</identifier>
    <identifier type="doi">10.18653/v1/2025.acl-long.1258</identifier>
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        <date>2025-07</date>
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            <start>25910</start>
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%0 Conference Proceedings
%T Do Language Models Have Semantics? On the Five Standard Positions
%A Søgaard, Anders
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F sogaard-2025-language
%X We identify five positions on whether large language models (LLMs) and chatbots can be said to exhibit semantic understanding. These positions differ in whether they attribute semantics to LLMs and/or chatbots trained on feedback, what kind of semantics they attribute (inferential or referential), and in virtue of what they attribute referential semantics (internal or external causes). This allows for 2⌃⌃4=16 logically possible positions, but we have only seen people argue for five of these. Based on a pairwise comparison of these five positions, we conclude that the better theory of semantics in large language models is, in fact, a sixth combination: Both large language models and chatbots have inferential and referential semantics, grounded in both internal and external causes.
%R 10.18653/v1/2025.acl-long.1258
%U https://aclanthology.org/2025.acl-long.1258/
%U https://doi.org/10.18653/v1/2025.acl-long.1258
%P 25910-25922

Markdown (Informal)

[Do Language Models Have Semantics? On the Five Standard Positions](https://aclanthology.org/2025.acl-long.1258/) (Søgaard, ACL 2025)

• Do Language Models Have Semantics? On the Five Standard Positions (Søgaard, ACL 2025)

ACL

• Anders Søgaard. 2025. Do Language Models Have Semantics? On the Five Standard Positions. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25910–25922, Vienna, Austria. Association for Computational Linguistics.