An End-to-End Ukrainian RAG for Local Deployment. Optimized Hybrid Search and Lightweight Generation

  1. Mykola Trokhymovych, Yana Oliinyk, Nazarii Nyzhnyk
    Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026), 2026
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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.

Citation

@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://aclanthology.org/2026.unlp-1.19/},
  pages = {223--229},
  isbn = {979-8-89176-359-3},
}

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