Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models
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Computer Science > Computation and Language
Title:Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models
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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.29278 [cs.CL] |
| (or arXiv:2605.29278v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29278
arXiv-issued DOI via DataCite (pending registration)
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