@inproceedings{nahin-etal-2025-titullms,
    title = "{T}itu{LLM}s: A Family of {B}angla {LLM}s with Comprehensive Benchmarking",
    author = "Nahin, Shahriar Kabir  and
      Nandi, Rabindra Nath  and
      Sarker, Sagor  and
      Muhtaseem, Quazi Sarwar  and
      Kowsher, Md  and
      Shill, Apu Chandraw  and
      Ibrahim, Md  and
      Menon, Mehadi Hasan  and
      Muntasir, Tareq Al  and
      Alam, Firoj",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.1279/",
    pages = "24922--24940",
    ISBN = "979-8-89176-256-5",
    abstract = "In this paper, we present TituLLMs, the first large pretrained Bangla LLMs, available in 1b and 3b parameter sizes. Due to computational constraints during both training and inference, we focused on smaller models. To train TituLLMs, we collected a pretraining dataset of approximately {\ensuremath{\sim}} 37 billion tokens. We extended the Llama-3.2 tokenizer to incorporate language- and culture-specific knowledge, which also enables faster training and inference. There was a lack of benchmarking datasets to benchmark LLMs for Bangla. To address this gap, we developed five benchmarking datasets. We benchmarked various LLMs, including TituLLMs, and demonstrated that TituLLMs outperforms its initial multilingual versions. However, this is not always the case, highlighting the complexities of language adaptation. Our work lays the groundwork for adapting existing multilingual open models to other low-resource languages. To facilitate broader adoption and further research, we have made the TituLLMs models and benchmarking datasets publicly available."
}