@inproceedings{bhattacharyya-bhattacharya-2025-banglabyt5,
    title = "{B}angla{B}y{T}5: Byte-Level Modelling for {B}angla",
    author = "Bhattacharyya, Pramit  and
      Bhattacharya, Arnab",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.297/",
    doi = "10.18653/v1/2025.findings-emnlp.297",
    pages = "5551--5560",
    ISBN = "979-8-89176-335-7",
    abstract = "Large language models (LLMs) have achievedremarkable success across various natural lan-guage processing tasks. However, most LLMmodels use traditional tokenizers like BPE andSentencePiece, which fail to capture the finernuances of a morphologically rich languagelike Bangla (Bengali). In this work, we introduce BanglaByT5, the first byte-level encoder-decoder model explicitly tailored for Bangla.Built upon a small variant of Google{'}s ByT5architecture, BanglaByT5 is pre-trained on a14GB curated corpus combining high-qualityliterary and newspaper articles. Through zero-shot and supervised evaluations across gen-erative and classification tasks, BanglaByT5demonstrates competitive performance, surpassing several multilingual and larger models.Our findings highlight BanglaByT5{'}s potentialas a lightweight yet powerful tool for BanglaNLP, particularly in resource-constrained orscalable environments. BanglaByT5 is pub-licly available for download from https://huggingface.co/Vacaspati/BanglaByT5."
}