@inproceedings{tamburini-2024-complexifying,
    title = "Complexifying {BERT} Using {L}o{RA} Adapters",
    author = "Tamburini, Fabio",
    editor = "Dell'Orletta, Felice  and
      Lenci, Alessandro  and
      Montemagni, Simonetta  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
    month = dec,
    year = "2024",
    address = "Pisa, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.clicit-1.102/",
    pages = "948--954",
    ISBN = "979-12-210-7060-6",
    abstract = "This paper presents the first results of a pilot study for transforming a real-valued pre-trained transformer encoder into a complex-valued one. Following recent findings about pre-training using LoRA, the main idea is to employ complex-valued LoRA adapters to make the trick and continue the pre-training of a given Italian model for setting up the adapters. After pre-training, the proposed complex-valued model has been evaluated on a standardised benchmark for Italian natural-language understanding obtaining very encouraging results."
}