  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.
