Unlocking the Global Synergies in Low-Rank Adapters
<|reference_start|>Unlocking the Global Synergies in Low-Rank Adapters: Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the limited LoRA trainable parameters across the model for better fine-tuned performance. In addition to the allocation for the standard LoRA-adapted models, we also demonstrate the efficacy of HeteroLoRA by performing the allocation in a more challenging search space that includes LoRA modules and LoRA-adapted shortcut connections. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge. For example, on MRPC, we see an improvement of 1.6% in accuracy with similar training parameter budget. We will open-source our algorithm once the paper is accepted.<|reference_end|>
@article{zhang2024unlocking,
    title={Unlocking the Global Synergies in Low-Rank Adapters},
    author={Zixi Zhang, Cheng Zhang, Xitong Gao, Robert D. Mullins, George A.
  Constantinides, Yiren Zhao},
    journal={arXiv preprint arXiv:2406.14956},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14956},
    primaryClass={cs.LG cs.CL}
}