MoR: Mixture of Ranks for Low-Rank Adaptation Tuning

• URL: http://arxiv.org/abs/2410.13408v2
• Date: Fri, 18 Oct 2024 03:05:01 GMT
• Title: MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
• Authors: Chuanyu Tang, Yilong Chen, Zhenyu Zhang, Junyuan Shang, Wenyuan Zhang, Yong Huang, Tingwen Liu,
• Abstract summary: Low-Rank Adaptation (LoRA) drives research to align its performance with full fine-tuning. MoE-style LoRA methods substantially increase parameters and inference latency. We introduce Mixture of Ranks (MoR), which learns rank-specific information for different tasks based on input. MoR achieves impressive results, with MoR delivering a 1.31% performance improvement while using only 93.93% of the parameters compared to baseline methods.
• Score: 18.102354643796826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-Rank Adaptation (LoRA) drives research to align its performance with full fine-tuning. However, significant challenges remain: (1) Simply increasing the rank size of LoRA does not effectively capture high-rank information, which leads to a performance bottleneck.(2) MoE-style LoRA methods substantially increase parameters and inference latency, contradicting the goals of efficient fine-tuning and ease of application. To address these challenges, we introduce Mixture of Ranks (MoR), which learns rank-specific information for different tasks based on input and efficiently integrates multi-rank information. We firstly propose a new framework that equates the integration of multiple LoRAs to expanding the rank of LoRA. Moreover, we hypothesize that low-rank LoRA already captures sufficient intrinsic information, and MoR can derive high-rank information through mathematical transformations of the low-rank components. Thus, MoR can reduces the learning difficulty of LoRA and enhances its multi-task capabilities. MoR achieves impressive results, with MoR delivering a 1.31\% performance improvement while using only 93.93\% of the parameters compared to baseline methods.

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