Parameter-Efficient Fine-tuning (PEFT) techniques have become essential in machine learning. They allow large models to be adapted to new tasks without using extensive computational resources. PEFT techniques aim to make the adaptation process more efficient and accessible by fine-tuning only a small subset of parameters while keeping the majority of the model fixed. This approach is essential for deploying large underlying models that are otherwise constrained by high computational costs and large parameter counts.
The central problem addressed in this work is the noticeable performance gap between low-rank adaptation methods such as LoRA and full fine-tuning of machine learning models. Although LoRA (Low-Rank Adaptation) is known for its efficiency, it often underperforms when compared to fully fine-tuned models. This discrepancy limits the broader application of LoRA in various domains where high performance is critical. The challenge is to make LoRA as effective as full fine-tuning while retaining its parameter-efficiency advantages.
Researchers have explored a variety of techniques. Current PEFT techniques include adapter tuning and prompt tuning. Adapter tuning involves inserting small trainable modules (adapters) into certain layers of the model. These adapters are fine-tuned while the rest of the model remains fixed, significantly reducing the memory footprint required for fine-tuning. Prompt tuning, on the other hand, adapts the model by adding learnable prompts or tokens to the input data, without the need to directly change the model's parameters. Among these techniques, LoRA stands out by reparameterizing weight changes during fine-tuning into a product of two low-rank matrices, reducing the number of trainable parameters.
Researchers from the University of Science and Technology of China, the Institute of Automation, Chinese Academy of Sciences, and the University of the Chinese Academy of Sciences LoRA ProThis new method bridges the performance gap between LoRA and full fine-tuning. LoRA-Pro enhances LoRA's optimization process by introducing an “equivalent gradient.” This concept allows researchers to measure the difference in the optimization process between LoRA and full fine-tuning, and then minimize that difference to improve performance. This ensures that LoRA-Pro's fine-tuning process closely mimics full fine-tuning.
LoRA-Pro defines the equivalent gradient as a virtual gradient that represents the gradient of the original matrix after a low-rank approximation, even though it cannot be trained directly. This gradient is derived from the gradients of the low-rank matrices A and B used in LoRA. During optimization, LoRA-Pro minimizes the difference between the equivalent gradient and the gradient obtained from full fine-tuning. This is achieved by choosing appropriate gradients for matrices A and B, formulating the problem as an optimization task, and deriving a theoretical solution for updating these matrices. The closed-form solution provided by LoRA-Pro ensures that the equivalent gradient closely matches the optimization dynamics of full fine-tuning, improving the overall effectiveness of LoRA.
The effectiveness of LoRA-Pro was verified through extensive experiments on natural language processing tasks. The method was tested on the T5 base model using a subset of the GLUE dataset. Results show that LoRA-Pro achieved the highest scores on three out of five datasets, with an average score that exceeded standard LoRA by 6.72%. Specifically, LoRA-Pro demonstrated superior performance, scoring 86.92% on MNLI, 94.46% on SST-2, and 87.50% on MRPC. These results highlight that LoRA-Pro can narrow the performance gap through thorough fine-tuning, providing a significant improvement over existing PEFT methods.
In conclusion, the introduction of LoRA-Pro marks a major advancement in parameter-efficient fine-tuning. By addressing the shortcomings of LoRA's optimization and introducing the concept of equivalent gradients, researchers have developed a method to bridge the performance gap between LoRA and full fine-tuning. Extensive experimental validation confirms that LoRA-Pro maintains the efficiency of LoRA and achieves performance levels close to full fine-tuning. This makes LoRA-Pro a valuable tool for deploying large-scale foundational models in a more resource-efficient manner.
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