@inproceedings{ceritli-etal-2025-hydraopt,
    title = "{H}ydra{O}pt: Navigating the Efficiency-Performance Trade-off of Adapter Merging",
    author = "Ceritli, Taha  and
      Bohdal, Ondrej  and
      Ozay, Mete  and
      Moon, Jijoong  and
      Lee, Kyenghun  and
      Ko, Hyeonmok  and
      Michieli, Umberto",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1365/",
    pages = "26875--26897",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models (LLMs) often leverage adapters, such as low-rank-based adapters, to achieve strong performance on downstream tasks. However, storing a separate adapter for each task significantly increases memory requirements, posing a challenge for resource-constrained environ ments such as mobile devices. Although model merging techniques can reduce storage costs, they typically result in substantial performance degradation. In this work, we introduce HydraOpt, a new model merging technique that capitalizes on the inherent similarities between the matrices of low-rank adapters. Unlike existing methods that produce a fixed trade-off between storage size and performance, HydraOpt allows us to navigate this spectrum of efficiency and performance. Our experiments show that HydraOpt significantly reduces storage size (48{\%} reduction) compared to storing all adapters, while achieving competitive performance (0.2-1.8{\%} drop). Furthermore, it outperforms existing merging techniques in terms of performance at the same or slightly worse storage efficiency."
}