@inproceedings{boros-etal-2024-fine,
    title = "Fine-Tuning and Retrieval Augmented Generation for Question Answering Using Affordable Large Language Models",
    author = "Boros, Tiberiu  and
      Chivereanu, Radu  and
      Dumitrescu, Stefan  and
      Purcaru, Octavian",
    editor = "Romanyshyn, Mariana  and
      Romanyshyn, Nataliia  and
      Hlybovets, Andrii  and
      Ignatenko, Oleksii",
    booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.unlp-1.10",
    pages = "75--82",
    abstract = "We present our proposed system named Sherlock to UNLP 2024 Shared Task on Question Answering winning first place. We employ a mix of methods, from using automatically translated datasets to perform supervised fine-tuning and direct preference optimization on instruction-tuned models, to model weight merging and retrieval augmented generation. We present and motivate our chosen sequence of steps, as well as an ablation study to understand the effect of each additional step. The resulting model and code are made publicly available (download links provided in the paper).",
}

We present our proposed system named Sherlock to UNLP 2024 Shared Task on Question Answering winning first place. We employ a mix of methods, from using automatically translated datasets to perform supervised fine-tuning and direct preference optimization on instruction-tuned models, to model weight merging and retrieval augmented generation. We present and motivate our chosen sequence of steps, as well as an ablation study to understand the effect of each additional step. The resulting model and code are made publicly available (download links provided in the paper).
Boros, Tiberiu Chivereanu, Radu Dumitrescu, Stefan Purcaru, Octavian
Fine-Tuning and Retrieval Augmented Generation for Question Answering Using Affordable Large Language Models