@inproceedings{wu-etal-2025-convolutional,
    title = "Convolutional {L}o{RA} Aggregation for Unseen Tasks Adaptation",
    author = "Wu, Xinhao  and
      Liu, Jialin  and
      Duan, Yutai  and
      Liu, Jie",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.198/",
    doi = "10.18653/v1/2025.findings-emnlp.198",
    pages = "3702--3714",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent studies have increasingly explored the combination of existing LoRA modules for effective adaptation to unseen tasks in data-scarce scenarios. However, current LoRA selection methods typically rely on a few task samples, making it difficult to capture the full scope of task-relevant information. Furthermore, even after selection, a knowledge gap remains between the selected LoRA modules and the target task, which existing coarse-grained LoRA aggregation strategies struggle to bridge. To address these challenges, we propose Selection and Convolution for LoRA aggregation (SC-LoRA), a two-stage framework that first selects appropriate LoRA modules based on parameter clustering and then aggregates them using a convolutional LoRA aggregator. Our LoRA selection strategy ensures comprehensive coverage of task-relevant LoRA modules by leveraging their distance in the parameter space. Building on this, the convolutional LoRA aggregator extracts useful knowledge in a fine-grained manner, seamlessly bridging the gap to the target task. Our experiments demonstrate that SC-LoRA excels in aggregating multiple LoRA modules for effective adaptation to unseen tasks."
}