@inproceedings{chen-etal-2025-parameter,
    title = "Parameter-Efficient Fine-Tuning via Circular Convolution",
    author = "Chen, Aochuan  and
      Cheng, Jiashun  and
      Liu, Zijing  and
      Gao, Ziqi  and
      Tsung, Fugee  and
      Li, Yu  and
      Li, Jia",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.102/",
    pages = "2004--2019",
    ISBN = "979-8-89176-256-5",
    abstract = "Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices $\mathbf A$ and $\mathbf B$ to represent weight changes (i.e., $\Delta \mathbf W = \mathbf B \mathbf A$). This method reduces trainable parameters and mitigates heavy memory consumption associated with full delta matrices by sequentially multiplying $\mathbf A$ and $\mathbf B$ with the activation. Despite its success, the intrinsic low-rank characteristic may limit its performance. Although several variants have been proposed to address this issue, they often overlook the crucial computational and memory efficiency brought by LoRA. In this paper, we propose Circular Convolution Adaptation (C$^3$A), which not only achieves high-rank adaptation with enhanced performance but also excels in both computational power and memory utilization. Extensive experiments demonstrate that C$^3$A consistently outperforms LoRA and its variants across various fine-tuning tasks."
}