CoLA: Collaborative Low-Rank Adaptation

TL;DR

CoLA introduces a flexible LoRA-based parameter-efficient fine-tuning method with collaborative strategies, significantly improving multi-task learning performance and robustness, especially in low-sample scenarios.

Contribution

It proposes a novel flexible LoRA architecture with an efficient initialization and collaborative strategies to enhance multi-task fine-tuning of large language models.

Findings

Outperforms existing PEFT methods in low-sample scenarios

Demonstrates robustness and effectiveness across multiple tasks

Provides publicly available data and code

Abstract

The scaling law of Large Language Models (LLMs) reveals a power-law relationship, showing diminishing return on performance as model scale increases. While training LLMs from scratch is resource-intensive, fine-tuning a pre-trained model for specific tasks has become a practical alternative. Full fine-tuning (FFT) achieves strong performance; however, it is computationally expensive and inefficient. Parameter-efficient fine-tuning (PEFT) methods, like LoRA, have been proposed to address these challenges by freezing the pre-trained model and adding lightweight task-specific modules. LoRA, in particular, has proven effective, but its application to multi-task scenarios is limited by interference between tasks. Recent approaches, such as Mixture-of-Experts (MOE) and asymmetric LoRA, have aimed to mitigate these issues but still struggle with sample scarcity and noise interference due to their fixed structure. In response, we propose CoLA, a more flexible LoRA architecture with an efficient initialization scheme, and introduces three collaborative strategies to enhance performance by better utilizing the quantitative relationships between matrices $A$ and $B$. Our experiments demonstrate the effectiveness and robustness of CoLA, outperforming existing PEFT methods, especially in low-sample scenarios. Our data and code are fully publicly available at https://github.com/zyy-2001/CoLA.