The provided text is an academic paper titled "Active Use of Latent Constituency Representation in both Humans and Large Language Models," which explores how sentences are internally represented in both the human brain and large language models (LLMs) like ChatGPT. The authors introduce a novel one-shot learning word deletion task where participants infer a deletion rule from a single example; they found that both humans and LLMs tend to delete a complete linguistic constituent rather than a nonconstituent word string, suggesting that latent, hierarchical linguistic structures emerge in both. Furthermore, the study demonstrates that the deletion behavior can be used to reconstruct a constituency tree representation that is structurally consistent with linguistically defined trees. The research also investigates how language-dependent rules are inferred and finds that native speakers primarily rely on syntactic structure over semantic plausibility in this task. Source: https://arxiv.org/pdf/2405.18241