# Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models

arXiv: https://arxiv.org/abs/2605.29278 · HTML: https://mlsanity.com/paper/2605.29278/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 7 of 7)

- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Large Language Models Pass the Turing Test](https://mlsanity.com/paper/2503.23674/) (2025)
- [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)
- [Do language models accommodate their users? A study of linguistic convergence](https://mlsanity.com/paper/2508.03276/) (2025)

## Built on by (most cited first, 2 of 2)

- [Instruction-Tuned Models Locally Reuse Human Syntax More Than Humans Do](https://mlsanity.com/paper/2607.26015/) (2026)
- [Talking Past the Machine: Morality, Politeness, and Alignment in Human-AI Dialogue](https://mlsanity.com/paper/2609.21401/) (2026)

## Abstract (arXiv)

As LLMs become increasingly integrated into daily life, understanding how their presence will shape human linguistic behavior is an open question. We present a large-scale study of linguistic convergence in human-LLM dialogue, examining how humans and LLMs accommodate each other's linguistic style during multi-turn conversations. Using an asymmetric convergence metric on WildChat, a corpus of real-world ChatGPT transcripts, we find that while LLMs significantly overconverge toward their users on both function word and open-class features across eight languages, human convergence rates in this setting are broadly consistent with human-human baselines. These findings suggest that accommodation in human-LLM dialogue is asymmetric: while LLMs dramatically overfit to their users' style, humans linguistically accommodate LLMs no differently than they would another person.