研究发现语言模型会过度模仿人类，而人类对模型的适应程度与人际交流无异。
Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models
- 用非对称度量分析真实对话数据，检测双方语言风格的相互适应
- 语言模型在8种语言中显著过度模仿用户，功能词和开放类词汇均明显趋同
- 人类对语言模型的适应与对人的适应相当，未表现出特殊适应行为
随着大语言模型日益融入日常生活，其如何影响人类语言行为仍是开放问题。本文基于真实世界ChatGPT对话数据集WildChat，开展大规模研究，分析人与大语言模型在多轮对话中语言风格的相互适应。采用非对称收敛度量方法，发现尽管大语言模型在八种语言中均显著过度向用户靠拢，涵盖功能词与开放类词汇特征，但人类在此情境下的语言适应率与人类间对话基准水平基本一致。结果表明，人机对话中的语言适应具有不对称性：语言模型会剧烈模仿用户风格，而人类对模型的适应程度与对待其他人类并无差异。
原文摘要 · Abstract (English)
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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