The paper presents GLoRA, an advanced approach for universal parameter-efficient fine-tuning tasks. 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. It outperforms previous methods in natural, specialized, and structured benchmarks, achieving superior accuracy with fewer parameters and computations on various datasets.

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