While a large body of literature suggests that large language models (LLMs) acquire rich linguistic representations, little is known about whether they adapt to linguistic biases in a human-like way. The present study probes this question by asking whether LLMs display human-like referential biases using stimuli and procedures from real psycholinguistic experiments. Recent psycholinguistic studies suggest that humans adapt their referential biases with recent exposure to referential patterns; closely replicating three relevant psycholinguistic experiments from Johnson & Arnold (2022) in an in-context learning (ICL) framework, we found that InstructGPT adapts its pronominal interpretations in response to the frequency of referential patterns in the local discourse, though in a limited fashion: adaptation was only observed relative to syntactic but not semantic biases. By contrast, FLAN-UL2 fails to generate meaningful patterns. Our results provide further evidence that contemporary LLMs discourse representations are sensitive to syntactic patterns in the local context but less so to semantic patterns. Our data and code are available at \url{https://github.com/zkx06111/llm_priming}.

翻译：尽管大量文献表明大型语言模型（LLMs）能够习得丰富的语言表征，但关于它们是否以类人方式适应语言偏好的研究仍十分有限。本研究通过采用真实心理语言学实验中的刺激材料与实验程序，探究LLMs是否展现出类似人类的指代偏好。近期心理语言学研究指出，人类会依据近期接触的指代模式调整自身指代偏好；我们通过情境学习（ICL）框架严格复现Johnson & Arnold（2022）三项相关心理语言学实验后发现，InstructGPT能够根据局部语篇中指代模式的频率调整其代词解读，但这种调整存在局限性：仅观察到对句法偏好的适应，而非语义偏好。相比之下，FLAN-UL2未能生成具有统计学意义的模式。我们的研究结果进一步证明，当代LLMs的语篇表征对局部语境的句法模式较为敏感，但对语义模式的敏感度较低。实验数据与代码已开源发布于\url{https://github.com/zkx06111/llm_priming}。