@inproceedings{shi-etal-2026-samora,
    title = "{SAM}o{RA}: Semantic-Aware Mixture of {L}o{RA} Experts for Task-Adaptive Learning",
    author = "Shi, Boyan  and
      Chen, Wei  and
      Zhao, Shuyuan  and
      Shen, Junfeng  and
      Guo, Shengnan  and
      Wang, Shaojiang  and
      Wan, Huaiyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1404/",
    pages = "28173--28188",
    ISBN = "979-8-89176-395-1",
    abstract = "The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capabilities of Large Language Models. However, existing methods face two primary challenges: (1)Imprecise Routing in the current MoE-LoRA method fails to explicitly match input semantics with expert capabilities, leading to weak expert specialization. (2)Uniform weight fusion strategies struggle to provide adaptive update strengths, overlooking the varying complexity of different tasks. To address these limitations, we propose \textbf{SAMoRA} (\textbf{S}emantic-\textbf{A}ware \textbf{M}ixture \textbf{o}f Lo\textbf{RA} Experts), a novel parameter-efficient fine-tuning framework tailored for task-adaptive learning. Specifically, A \textbf{Semantic-Aware Router} is proposed to explicitly align textual semantics with the most suitable experts for precise routing. A \textbf{Task-Adaptive Scaling} mechanism is designed to regulate expert contributions based on specific task requirements dynamically. In addition, a novel regularization objective is proposed to jointly promote expert specialization and effective scaling. Extensive experiments on multiple multi-task benchmarks demonstrate that SAMoRA significantly outperforms the state-of-the-art methods and holds excellent task generalization capabilities. Code is available at \url{https://github.com/boyan-code/SAMoRA}"
}