Humans acquire language through implicit learning, absorbing complex patterns without explicit awareness. While LLMs demonstrate impressive linguistic capabilities, it remains unclear whether they exhibit human-like pattern recognition during in-context learning at inferencing level. We adapted three classic artificial language learning experiments spanning morphology, morphosyntax, and syntax to systematically evaluate implicit learning at inferencing level in two state-of-the-art OpenAI models: gpt-4o and o3-mini. Our results reveal linguistic domain-specific alignment between models and human behaviors, o3-mini aligns better in morphology while both models align in syntax.

翻译：人类通过隐式学习获得语言能力，能够在无明确意识的情况下吸收复杂模式。尽管大语言模型展现出令人瞩目的语言能力，但其在推理层面的上下文学习过程中是否表现出类人的模式识别能力仍不明确。我们改编了涵盖形态学、形态句法学和句法学的三个经典人工语言学习实验，系统评估了OpenAI两个前沿模型（gpt-4o与o3-mini）在推理层面的隐式学习能力。实验结果表明模型与人类行为存在语言学领域特异性对齐：o3-mini在形态学领域对齐度更高，而两个模型在句法学领域均表现出对齐。