@inproceedings{durandard-etal-2025-llms,
title = "{LLM}s stick to the point, humans to style: Semantic and Stylistic Alignment in Human and {LLM} Communication",
author = "Durandard, No{\'e} and
Dhawan, Saurabh and
Poibeau, Thierry",
editor = "B{\'e}chet, Fr{\'e}d{\'e}ric and
Lef{\`e}vre, Fabrice and
Asher, Nicholas and
Kim, Seokhwan and
Merlin, Teva",
booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2025",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sigdial-1.16/",
pages = "206--213",
abstract = "This study investigates differences in linguistic accommodation{---}changes in language use and style that individuals make to align with their dialogue partners{---}in human and LLM communication. Specifically, it contrasts semantic and stylistic alignment within question-answer pairs in terms of whether the answer was given by a human or an LLM. Utilizing embedding-based measures of linguistic similarity, we find that LLM-generated answers demonstrate higher semantic similarity{---}reflecting close conceptual alignment with the input questions{---}but relatively lower stylistic similarity. Human-written answers exhibit a reverse pattern, with lower semantic but higher stylistic similarity to the respective questions. These findings point to contrasting linguistic accommodation strategies evident in human and LLM communication, with implications for furthering personalization, social attunement, and engagement in human-AI dialogue."
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<abstract>This study investigates differences in linguistic accommodation—changes in language use and style that individuals make to align with their dialogue partners—in human and LLM communication. Specifically, it contrasts semantic and stylistic alignment within question-answer pairs in terms of whether the answer was given by a human or an LLM. Utilizing embedding-based measures of linguistic similarity, we find that LLM-generated answers demonstrate higher semantic similarity—reflecting close conceptual alignment with the input questions—but relatively lower stylistic similarity. Human-written answers exhibit a reverse pattern, with lower semantic but higher stylistic similarity to the respective questions. These findings point to contrasting linguistic accommodation strategies evident in human and LLM communication, with implications for furthering personalization, social attunement, and engagement in human-AI dialogue.</abstract>
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%0 Conference Proceedings
%T LLMs stick to the point, humans to style: Semantic and Stylistic Alignment in Human and LLM Communication
%A Durandard, Noé
%A Dhawan, Saurabh
%A Poibeau, Thierry
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F durandard-etal-2025-llms
%X This study investigates differences in linguistic accommodation—changes in language use and style that individuals make to align with their dialogue partners—in human and LLM communication. Specifically, it contrasts semantic and stylistic alignment within question-answer pairs in terms of whether the answer was given by a human or an LLM. Utilizing embedding-based measures of linguistic similarity, we find that LLM-generated answers demonstrate higher semantic similarity—reflecting close conceptual alignment with the input questions—but relatively lower stylistic similarity. Human-written answers exhibit a reverse pattern, with lower semantic but higher stylistic similarity to the respective questions. These findings point to contrasting linguistic accommodation strategies evident in human and LLM communication, with implications for furthering personalization, social attunement, and engagement in human-AI dialogue.
%U https://aclanthology.org/2025.sigdial-1.16/
%P 206-213
Markdown (Informal)
[LLMs stick to the point, humans to style: Semantic and Stylistic Alignment in Human and LLM Communication](https://aclanthology.org/2025.sigdial-1.16/) (Durandard et al., SIGDIAL 2025)
ACL