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        <title>Do Large Language Models Adapt to Language Variation across Socioeconomic Status?</title>
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    <name type="personal">
        <namePart type="given">Elisa</namePart>
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    <name type="personal">
        <namePart type="given">Mike</namePart>
        <namePart type="family">Zhang</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Amanda</namePart>
        <namePart type="family">Cercas Curry</namePart>
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            <title>Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects</title>
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    <abstract>Humans adjust their linguistic style to the audience they are addressing. However, the extent to which LLMs adapt to different social contexts is largely unknown. As these models increasingly mediate human-to-human communication, their failure to adapt to diverse styles can perpetuate stereotypes and marginalize communities whose linguistic norms are less closely mirrored by the models, thereby reinforcing social stratification. We study the extent to which LLMs integrate into social media communication across different socioeconomic status (SES) communities. We collect a novel dataset from Reddit and YouTube, stratified by SES. We prompt four LLMs with incomplete text from that corpus and compare the LLM-generated completions to the originals along 94 sociolinguistic metrics, including syntactic, rhetorical, and lexical features. LLMs modulate their style with respect to SES to only a minor extent, often resulting in approximation or caricature, and tend to emulate the style of upper SES more effectively. Our findings (1) show how LLMs risk amplifying linguistic hierarchies and (2) call into question their validity for agent-based social simulation, survey experiments, and any research relying on language style as a social signal.</abstract>
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%0 Conference Proceedings
%T Do Large Language Models Adapt to Language Variation across Socioeconomic Status?
%A Bassignana, Elisa
%A Zhang, Mike
%A Hovy, Dirk
%A Cercas Curry, Amanda
%S Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F bassignana-etal-2026-large
%X Humans adjust their linguistic style to the audience they are addressing. However, the extent to which LLMs adapt to different social contexts is largely unknown. As these models increasingly mediate human-to-human communication, their failure to adapt to diverse styles can perpetuate stereotypes and marginalize communities whose linguistic norms are less closely mirrored by the models, thereby reinforcing social stratification. We study the extent to which LLMs integrate into social media communication across different socioeconomic status (SES) communities. We collect a novel dataset from Reddit and YouTube, stratified by SES. We prompt four LLMs with incomplete text from that corpus and compare the LLM-generated completions to the originals along 94 sociolinguistic metrics, including syntactic, rhetorical, and lexical features. LLMs modulate their style with respect to SES to only a minor extent, often resulting in approximation or caricature, and tend to emulate the style of upper SES more effectively. Our findings (1) show how LLMs risk amplifying linguistic hierarchies and (2) call into question their validity for agent-based social simulation, survey experiments, and any research relying on language style as a social signal.
%U https://aclanthology.org/2026.vardial-1.26/
%P 317-338

Markdown (Informal)

[Do Large Language Models Adapt to Language Variation across Socioeconomic Status?](https://aclanthology.org/2026.vardial-1.26/) (Bassignana et al., VarDial 2026)

• Do Large Language Models Adapt to Language Variation across Socioeconomic Status? (Bassignana et al., VarDial 2026)

ACL

• Elisa Bassignana, Mike Zhang, Dirk Hovy, and Amanda Cercas Curry. 2026. Do Large Language Models Adapt to Language Variation across Socioeconomic Status?. In Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 317–338, Rabat, Morocco. Association for Computational Linguistics.