@inproceedings{lee-etal-2025-star,
    title = "{STAR}: Spectral Truncation and Rescale for Model Merging",
    author = "Lee, Yu-Ang  and
      Ko, Ching-Yun  and
      Pedapati, Tejaswini  and
      Chung, I-Hsin  and
      Yeh, Mi-Yen  and
      Chen, Pin-Yu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-06/2025.naacl-short.42/",
    doi = "10.18653/v1/2025.naacl-short.42",
    pages = "496--505",
    ISBN = "979-8-89176-190-2",
    abstract = "Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning, and it has gained attention in various domains, including natural language processing (NLP). Despite the efficiency, a key challenge in model merging is the seemingly inevitable decrease in task performance as the number of models increases. In this paper, we propose **S**pectral **T**runcation **A**nd **R**escale (STAR) that aims at mitigating ``merging conflicts'' by truncating small components in the respective spectral spaces, which is followed by an automatic parameter rescaling scheme to retain the nuclear norm of the original matrix. STAR requires no additional inference on original training data and is robust to hyperparamater choice. We demonstrate the effectiveness of STAR through extensive model merging cases on diverse NLP tasks. Specifically, STAR works robustly across varying model sizes, and can outperform baselines by 4.2{\%} when merging 12 models on Flan-T5. Our code is publicly available at https://github.com/IBM/STAR."
}