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        <title>LLMs syntactically adapt their language use to their conversational partner</title>
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    <name type="personal">
        <namePart type="given">Florian</namePart>
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    <name type="personal">
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    <name type="personal">
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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
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            <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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            <namePart type="given">Mohammad</namePart>
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            <namePart type="family">Pilehvar</namePart>
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    <abstract>It has been frequently observed that human speakers align their language use with each other during conversations. In this paper, we study empirically whether large language models (LLMs) exhibit the same behavior of conversational adaptation.We construct a corpus of conversations between LLMs and find that two LLM agents end up making more similar syntactic choices as conversations go on, confirming that modern LLMs adapt their language use to their conversational partners in at least a rudimentary way.</abstract>
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        <date>2025-07</date>
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%0 Conference Proceedings
%T LLMs syntactically adapt their language use to their conversational partner
%A Kandra, Florian
%A Demberg, Vera
%A Koller, Alexander
%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 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F kandra-etal-2025-llms
%X It has been frequently observed that human speakers align their language use with each other during conversations. In this paper, we study empirically whether large language models (LLMs) exhibit the same behavior of conversational adaptation.We construct a corpus of conversations between LLMs and find that two LLM agents end up making more similar syntactic choices as conversations go on, confirming that modern LLMs adapt their language use to their conversational partners in at least a rudimentary way.
%R 10.18653/v1/2025.acl-short.68
%U https://aclanthology.org/2025.acl-short.68/
%U https://doi.org/10.18653/v1/2025.acl-short.68
%P 873-886

Markdown (Informal)

[LLMs syntactically adapt their language use to their conversational partner](https://aclanthology.org/2025.acl-short.68/) (Kandra et al., ACL 2025)

• LLMs syntactically adapt their language use to their conversational partner (Kandra et al., ACL 2025)

ACL

• Florian Kandra, Vera Demberg, and Alexander Koller. 2025. LLMs syntactically adapt their language use to their conversational partner. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 873–886, Vienna, Austria. Association for Computational Linguistics.