@inproceedings{lu-etal-2026-adams,
    title = "{A}dam{'}s Law: Textual Frequency Law on Large Language Models",
    author = "Lu, Hongyuan  and
      Li, Zixuan  and
      Zhang, Zefan  and
      Cao, Bowen  and
      Lam, Wai",
    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.188/",
    pages = "4079--4105",
    ISBN = "979-8-89176-390-6",
    abstract = "While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel research direction in terms of textual data frequency, which is an understudied topic. to the best of our knowledge. Our framework is composed of three units. First, this paper proposes \textbf{T}extual \textbf{F}requency \textbf{L}aw (TFL), which indicates that frequent textual data should be preferred for LLMs for both prompting and fine-tuning. Since many LLMs are closed-source in their training data, we propose using online resources to estimate the sentence-level frequency. We can then use an input paraphraser to paraphrase the input into a more frequent textual expression. Next, we propose \textbf{T}extual \textbf{F}requency \textbf{D}istillation (TFD) by querying LLMs to conduct story completion by further extending the sentences in the datasets, and the resulting corpora are used to adjust the initial estimation. Finally, we propose \textbf{C}urriculum \textbf{T}extual \textbf{F}requency \textbf{T}raining (CTFT) that fine-tunes LLMs in an increasing order of sentence-level frequency. Experiments are conducted on our curated dataset \textbf{T}extual \textbf{F}requency \textbf{P}aired \textbf{D}ataset (TFPD) on math reasoning and machine translation. Results indicate the effectiveness of our framework."
}