@inproceedings{hu-etal-2025-circuits,
    title = "Between Circuits and {C}homsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases",
    author = "Hu, Michael Y.  and
      Petty, Jackson  and
      Shi, Chuan  and
      Merrill, William  and
      Linzen, Tal",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.478/",
    pages = "9691--9709",
    ISBN = "979-8-89176-251-0",
    abstract = "Pretraining language models on formal language can improve their acquisition of natural language. Which features of the formal language impart an inductive bias that leads to effective transfer? Drawing on insights from linguistics and complexity theory, we hypothesize that effective transfer occurs when two conditions are met: the formal language should capture the dependency structures present in natural language, and it should remain within the computational limitations of the model architecture. We experiment with pre-pretraining (training on formal language before natural languages) on transformers and find that formal languages capturing hierarchical dependencies indeed enable language models to achieve lower loss on natural language and better linguistic generalization compared to other formal languages. We also find modest support for the hypothesis that the formal language should fall within the computational limitations of the architecture. Strikingly, pre-pretraining reduces loss more efficiently than training on a matched amount of natural language. For a 1B-parameter language model trained on roughly 1.6B tokens of natural language, pre-pretraining achieves the same loss and better linguistic generalization with a 33{\%} smaller token budget. Finally, we also give mechanistic evidence of transfer from formal tonatural language: attention heads acquired during pre-pretraining remain crucial for the model{'}s performance on syntactic evaluations."
}