Unlocking the Global Synergies in Low-Rank Adapters

• URL: http://arxiv.org/abs/2406.14956v1
• Date: Fri, 21 Jun 2024 08:10:03 GMT
• Title: Unlocking the Global Synergies in Low-Rank Adapters
• Authors: Zixi Zhang, Cheng Zhang, Xitong Gao, Robert D. Mullins, George A. Constantinides, Yiren Zhao,
• Abstract summary: 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. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge.
• Score: 20.32980343066711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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