@inproceedings{tampier-etal-2025-recombitext,
    title = "{R}ecombi{T}ext: Compositional Data Augmentation for Enhancing {LLM} Pre-Training Datasets in Low-Resource Scenarios",
    author = "Tampier, Alexander  and
      Thoma, Lukas  and
      Schoenegger, Loris  and
      Roth, Benjamin",
    editor = "Charpentier, Lucas  and
      Choshen, Leshem  and
      Cotterell, Ryan  and
      Gul, Mustafa Omer  and
      Hu, Michael Y.  and
      Liu, Jing  and
      Jumelet, Jaap  and
      Linzen, Tal  and
      Mueller, Aaron  and
      Ross, Candace  and
      Shah, Raj Sanjay  and
      Warstadt, Alex  and
      Wilcox, Ethan Gotlieb  and
      Williams, Adina",
    booktitle = "Proceedings of the First BabyLM Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.babylm-main.40/",
    pages = "548--565",
    ISBN = "TODO",
    abstract = "We introduce RecombiText Augmentation (RTA), a novel purely statistical NLP method for compositional data augmentation for data-efficient LLM pre-training in low-resource scenarios. RTA identifies lexically and semantically similar sentences within the corpus and generates synthetic sentence pairs from them while preserving underlying patterns from the corpus. We pre-train GPT-2 and RoBERTa language models on a domain-specific, low-resource corpus of 10 million words, with different proportions of augmented data. We compare our RTA-augmented model variants to a baseline model trained on the full original dataset. Zero-shot results show that the language models pre-trained on synthetic data improve in entity tracking, self-paced reading, and morphological generalization benchmarks. In other tasks, the performance is comparable to the baseline model. We demonstrate that it is possible to expand low-resource datasets by two- to four-fold without compromising benchmark performance, solely through statistical processing of the available data."
}