@inproceedings{trokhymovych-etal-2026-end,
    title = "An End-to-End {U}krainian {RAG} for Local Deployment. Optimized Hybrid Search and Lightweight Generation",
    author = "Trokhymovych, Mykola  and
      Oliinyk, Yana  and
      Nyzhnyk, Nazarii",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Fifth {U}krainian Natural Language Processing Conference ({UNLP} 2026)",
    month = may,
    year = "2026",
    address = "Lviv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-06/2026.unlp-1.19/",
    pages = "223--229",
    ISBN = "979-8-89176-359-3",
    abstract = "This paper presents a highly efficient Retrieval-Augmented Generation (RAG) system built specifically for Ukrainian document question answering, which achieved 2nd place in the UNLP 2026 Shared Task. Our solution features a custom two-stage search pipeline that retrieves relevant document pages, paired with a specialized Ukrainian language model fine-tuned on synthetic data to generate accurate, grounded answers. Finally, we compress the model for lightweight deployment. Evaluated under strict computational limits, our architecture demonstrates that high-quality, verifiable AI question answering can be achieved locally on resource-constrained hardware without sacrificing accuracy."
}