Do Large Language Models Adapt to Language Variation across Socioeconomic Status?

2026-02-12

AI Summary

This study addresses the limited adaptability of large language models (LLMs) to linguistic styles associated with varying socioeconomic status (SES) groups, which may reinforce linguistic hierarchies and marginalize low-SES communities. It presents the first systematic quantification of LLMs’ linguistic accommodation along the SES dimension by constructing stratified corpora from Reddit and YouTube and comparing generated texts from four major LLMs against original human-authored texts across 94 sociolinguistic features. The findings reveal that LLMs exhibit only minimal stylistic adaptation to different SES contexts, often overfitting to or stereotypically mimicking high-SES language patterns. These results highlight a significant limitation in LLMs’ capacity to model sociolinguistic diversity and challenge assumptions about their validity in tasks requiring realistic social simulation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Large Language Models
• Language Variation
• Socioeconomic Status
• Linguistic Style
• Social Stratification

Innovation

• socioeconomic status
• linguistic adaptation
• large language models
• sociolinguistic metrics
• language variation