Understanding how sentences are internally represented in the human brain, as well as in large language models (LLMs) such as ChatGPT, is a major challenge for cognitive science. Classic linguistic theories propose that the brain represents a sentence by parsing it into hierarchically organized constituents. In contrast, LLMs do not explicitly parse linguistic constituents and their latent representations remains poorly explained. Here, we demonstrate that humans and LLMs construct similar latent representations of hierarchical linguistic constituents by analyzing their behaviors during a novel one-shot learning task, in which they infer which words should be deleted from a sentence. Both humans and LLMs tend to delete a constituent, instead of a nonconstituent word string. In contrast, a naive sequence processing model that has access to word properties and ordinal positions does not show this property. Based on the word deletion behaviors, we can reconstruct the latent constituency tree representation of a sentence for both humans and LLMs. These results demonstrate that a latent tree-structured constituency representation can emerge in both the human brain and LLMs.

翻译：理解句子在人类大脑以及诸如ChatGPT等大型语言模型中的内部表征方式，是认知科学面临的重大挑战。经典语言学理论认为，大脑通过将句子解析为层次化组织的成分结构来表征语句。相比之下，大型语言模型并不显式解析语言成分，其潜在表征机制仍缺乏合理解释。本研究通过分析人类与大型语言模型在新型单次学习任务中的行为表现，证明两者能构建相似的层次化语言成分潜在表征。该任务要求被试推断句子中应删除哪些词语。实验发现，人类与大型语言模型均倾向于删除完整成分结构，而非非成分的词语序列。与此形成对照的是，仅具备词汇属性与序列位置信息的朴素序列处理模型未表现出此特性。基于词语删除行为模式，我们成功重建了人类与大型语言模型对句子的潜在成分树表征。这些结果表明，树状结构的潜在成分表征能够同时在人类大脑与大型语言模型中涌现。