@inproceedings{wang-etal-2025-malora,
    title = "{MAL}o{RA}: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning",
    author = "Wang, Xujia  and
      Zhao, Haiyan  and
      Wang, Shuo  and
      Wang, Hanqing  and
      Liu, Zhiyuan",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.findings-naacl.312/",
    pages = "5609--5626",
    ISBN = "979-8-89176-195-7",
    abstract = "Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA have significantly improved the adaptation of LLMs to downstream tasksin a resource-efficient manner. However, in multi-task scenarios, challenges such as training imbalance and the seesaw effect frequently emerge. Mixture-of-LoRA (MoLoRA), which combines LoRA with sparse Mixture-of-Experts, mitigates some of these issues by promoting task-specific learning among experts. Despite this, MoLoRA remains inefficient in terms of training speed, parameter utilization, and overall multi-task performance. In this paper, we propose Mixture of Asymmetric Low-Rank Adaptaion (MALoRA), a flexible fine-tuning framework that leverages asymmetric optimization among LoRA experts. MALoRA reduces the number of trainable parameters by 30{\%} to 48{\%}, increases training speed by 1.2x, and matches the computational efficiency of single-task LoRA models. Additionally, MALoRA addresses overfitting issues commonly seen in high-rank configurations, enhancing performance stability. Extensive experiments across diverse multi-task learning scenarios demonstrate that MALoRA consistently outperforms all baseline methods in both inter-domain and intra-domain tasks."
}