  With the rapid scaling of large language models (LLMs), serving numerous
low-rank adaptations (LoRAs) concurrently has become increasingly impractical,
leading to unaffordable costs and necessitating more parameter-efficient
finetuning methods. In this work, we introduce Partially Rotation-enhanced
Low-Rank Adaptation (PRoLoRA), an intra-layer sharing mechanism comprising four
essential components: broadcast reduction, rotation enhancement,
partially-sharing refinement, and rectified initialization strategy. As a
superset of LoRA, PRoLoRA retains its advantages, and effectively circumvent
the drawbacks of peer parameter-sharing methods with superior model capacity,
practical feasibility, and broad applicability. Empirical experiments
demonstrate the remarkably higher parameter efficiency of PRoLoRA in both
specific parameter budget and performance target scenarios, and its scalability
to larger LLMs. Notably, with one time less trainable parameters, PRoLoRA still
outperforms LoRA on multiple instruction tuning datasets. Subsequently, an
ablation study is conducted to validate the necessity of individual components
and highlight the superiority of PRoLoRA over three potential variants.
Hopefully, the conspicuously higher parameter efficiency can establish PRoLoRA
as a resource-friendly alternative to LoRA.
