@inproceedings{tanaka-ishii-2026-repeated,
    title = "Repeated Sequences Reveal Gaps between Large Language Models and Natural Language",
    author = "Tanaka-Ishii, Kumiko",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.379/",
    pages = "8367--8382",
    ISBN = "979-8-89176-390-6",
    abstract = "Evaluating whether large language models (LLMs) capture the structureof natural language beyond local fluency remains an open challenge.Existing evaluation methods, largely based on task performance orshort-context behavior, provide limited insight into the long-rangestatistical organization of generated text.We propose a complementary evaluation framework based on repeatedsubsequences. By analyzing their distribution across scales andrelating it to higher-order R{\'e}nyi entropies, we probe how textsreuse previously established structure under finite-lengthconditions. Experiments on human-written texts and length-matchedGPT-generated texts show that,while power-law models can describerestricted ranges of block length, the observed entropy growth isoften equally or better characterized by logarithmic{--}power forms.Across datasets, natural language exhibits stable entropy-growthpatterns over accessible ranges, with consistent average behavior despite variability across individual texts. In contrast,GPT-generated texts show systematic and statistically significantshifts in estimated exponents with model size.These results demonstrate that repeated-subsequence entropyprovides a quantitative structural diagnostic that revealssystematic differences in long-range organization,distinguishing natural language from state-of-the-art LLM outputsbeyond surface-level fluency."
}