@inproceedings{basu-etal-2025-garud,
    title = "{GAR}u{D}: Guided Alignment of Representations using Distillation for Ultra-Low-Resource Languages",
    author = "Basu, Debarchan  and
      Bhardwaj, Shashwat  and
      Sharma, Vaibhav  and
      Singh, Pooja  and
      Kumar, Sandeep",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.117/",
    pages = "1867--1880",
    ISBN = "979-8-89176-303-6",
    abstract = "The vast majority of the world{'}s languages, particularly low-resource and indigenous ones like Bhili, remain critically underserved by modern language technologies. The primary bottleneck is the lack of large-scale corpora required for standard pre-training. To address this gap, we introduce cross-lingual contrastive distillation, a novel and data-efficient, compute-efficient paradigm for creating powerful language models without a massive monolingual corpus. Our method adapts a pre-existing multilingual model (MuRIL) by using a fixed, expert teacher model (HindBERT) to distill semantic knowledge from a related high-resource language (Hindi) via a contrastive objective on a modest parallel corpus. Through comprehensive experiments, we show that our resulting model, GARuD-Bhili, significantly outperforms strong zero-shot and MLM-only baselines on a suite of evaluations, including intrinsic language modeling, downstream sentiment analysis, and cross-lingual benchmarks (Tatoeba, XNLI). Our work presents a generalizable and scalable blueprint for linguistic empowerment, offering a practical pathway to develop robust language technologies for other underserved languages globally."
}