# Do language models accommodate their users? A study of linguistic convergence

arXiv: https://arxiv.org/abs/2508.03276 · HTML: https://mlsanity.com/paper/2508.03276/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations](https://mlsanity.com/paper/2109.14989/) (2021)
- [Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models](https://mlsanity.com/paper/2408.06518/) (2024)
- [LLMs syntactically adapt their language use to their conversational partner](https://mlsanity.com/paper/2503.07457/) (2025)
- [Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions](https://mlsanity.com/paper/2106.01170/) (2021)
- [Chatbots language design: the influence of language variation on user experience](https://mlsanity.com/paper/2101.11089/) (2021)

## Built on by (most cited first, 7 of 7)

- [Markovian Generation Chains in Large Language Models](https://mlsanity.com/paper/2603.11228/) (2026)
- [Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models](https://mlsanity.com/paper/2605.29278/) (2026)
- [A Cross-lingual Comparison of Human and Classification Model Entrainment Behavior in Code-switched Speech Settings](https://mlsanity.com/paper/2607.25202/) (2026)
- [Instruction-Tuned Models Locally Reuse Human Syntax More Than Humans Do](https://mlsanity.com/paper/2607.26015/) (2026)
- [Alignment by Stereotyping: How LLMs Sacrifice Individual Distinctiveness for Cultural Adaptation](https://mlsanity.com/paper/2609.05993/) (2026)
- [Framing the Narrative: Ideological Mimicry in Large Language Models](https://mlsanity.com/paper/2609.38256/) (2026)
- [Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs](https://mlsanity.com/paper/2610.01428/) (2026)

## Abstract (arXiv)

While large language models (LLMs) are generally considered proficient in generating language, how similar their language usage is to that of humans remains understudied. In this paper, we test whether models exhibit linguistic convergence, a core pragmatic element of human language communication: do models adapt, or converge, to the linguistic patterns of their user? To answer this, we systematically compare model completions of existing dialogues to original human responses across sixteen language models, three dialogue corpora, and various stylometric features. We find that models strongly converge to the conversation's style, often significantly overfitting relative to the human baseline. While convergence patterns are often feature-specific, we observe consistent shifts in convergence across modeling settings, with instruction-tuned and larger models converging less than their pretrained and smaller counterparts. Given the differences in human and model convergence patterns, we hypothesize that the underlying mechanisms driving these behaviors are very different.