One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
Authors: name: Arnav Chavan, name: Zhuang Liu, name: Deepak Gupta, name: Eric Xing, name: Zhiqiang Shen

We present Generalized LoRA (GLoRA), an advanced approach for universal
parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA),
GLoRA employs a generalized prompt module to optimize pre-trained model weights
and adjust intermediate activations, providing more flexibility and capability
across diverse tasks and datasets. Moreover, GLoRA facilitates efficient
parameter adaptation by employing a scalable, modular, layer-wise structure
search that learns individual adapter of each layer. Originating from a unified
mathematical formulation, GLoRA exhibits strong transfer learning, few-shot
learning and domain generalization abilities, as it adjusts to new tasks
through additional dimensions on weights and activations. Comprehensive
experiments demonstrate that GLoRA outperforms all previous methods in natural,
specialized, and structured benchmarks, achieving superior accuracy with fewer
parameters and computations on various datasets. Furthermore, our structural
re-parameterization design ensures that GLoRA incurs no extra inference cost,
rendering it a practical solution for resource-limited applications. Code is
available at: https://github.com/Arnav0400/ViT-Slim/tree/master/GLoRA.