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

• URL: http://arxiv.org/abs/2605.29278v1
• Date: Thu, 28 May 2026 02:57:05 GMT
• Title: Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models
• Abstract summary: We study how humans and LLMs accommodate each other's linguistic style during multi-turn conversations.<n>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, human convergence rates in this setting are broadly consistent with human-human baselines.
• Score: 10.676434494520976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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