改进LoRA微调算法，让大模型更高效精准
Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks
- 用低秩适应策略减少计算资源消耗
- 在QQP任务上F1和MCC显著优于BERT等模型
- 适合资源受限场景与多任务学习
本研究提出一种基于改进LoRA微调算法的大语言模型优化方法，旨在提升自然语言处理任务中的准确率与计算效率。通过低秩适应策略对大语言模型进行微调，显著降低了计算资源消耗，同时保持预训练模型的强大能力。实验以QQP任务为评估场景，结果表明，改进的LoRA算法在准确率、F1分数和马修相关系数（MCC）上均显著优于BERT、RoBERTa、T5和GPT-4等传统模型。尤其在F1分数和MCC指标上，模型展现出更强的鲁棒性与判别能力，验证了改进LoRA算法在微调大规模预训练模型方面的潜力。此外，本文还探讨了该算法在其他自然语言处理任务中的应用前景，强调其在多任务学习及计算资源有限场景中的优势。未来研究可进一步优化LoRA微调策略，并拓展至更大规模预训练模型，以增强模型的泛化能力与任务适应性。
原文摘要 · Abstract (English)
This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing tasks. We fine-tune the large language model through a low-rank adaptation strategy, which significantly reduces the consumption of computing resources while maintaining the powerful capabilities of the pre-trained model. The experiment uses the QQP task as the evaluation scenario. The results show that the improved LoRA algorithm shows significant improvements in accuracy, F1 score, and MCC compared with traditional models such as BERT, Roberta, T5, and GPT-4. In particular, in terms of F1 score and MCC, our model shows stronger robustness and discrimination ability, which proves the potential of the improved LoRA algorithm in fine-tuning large-scale pre-trained models. In addition, this paper also discusses the application prospects of the improved LoRA algorithm in other natural language processing tasks, emphasizing its advantages in multi-task learning and scenarios with limited computing resources. Future research can further optimize the LoRA fine-tuning strategy and expand its application in larger-scale pre-trained models to improve the generalization ability and task adaptability of the model.
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