SparseLoRA: Rethinking the Efficiency of parameter in LoRA

Recent advancements in large-scale pretrained models have yielded impressive outcomes across various natural language processing (NLP) tasks. The application of transfer learning to these models for downstream tasks has emerged as a prevalent and effective strategy. Nevertheless, the complete fine-tuning of large models necessitates considerable computational resources and time, underscoring the demand for more economical fine-tuning methodologies. Low-Rank Adaptation (LoRA) has gained traction as a prominent technique that incorporates auxiliary parameters into specific layers of the network, thereby enabling the fine-tuning of pretrained models with reduced computational demands. Despite its advantages, LoRA frequently neglects the identification of more efficient ranks, leading to the inclusion of superfluous parameters and suboptimal fine-tuning outcomes. To address these challenges and enhance fine-tuning efficiency while minimizing unnecessary parameter redundancy, we introduce SparseLoRA. This innovative method employs parameter pruning strategies to selectively eliminate non-essential ranks, resulting in a more streamlined and efficient adapter. This approach not only mitigates the computational burden associated with the model but also enhances its performance on downstream tasks. We conducted comprehensive experiments focused on natural language understanding tasks, and the findings indicate that SparseLoRA achieves notable performance enhancements in comparison to other baseline models. Furthermore, SparseLoRA demonstrates a competitive performance relative to state-of-the-art models in evaluation.