Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks

2024-12-25

AI Summary

To address the inefficiency and poor robustness of large language models (LLMs) under resource-constrained fine-tuning scenarios, this paper proposes an enhanced Low-Rank Adaptation (LoRA) algorithm. The method introduces a structured low-rank adaptive module that preserves pre-trained knowledge integrity while drastically reducing trainable parameters. Comprehensive evaluation on the QQP benchmark demonstrates that our approach achieves significant improvements in both F1 score and Matthews Correlation Coefficient (MCC) over mainstream models—including BERT, RoBERTa, T5, and GPT-4—while reducing computational overhead by 42%–68%. Moreover, it exhibits superior generalization and robustness across multi-task transfer settings. The core contribution lies in a novel low-rank adaptation mechanism that jointly optimizes parameter efficiency, discriminative capability, and task-specific adaptability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Efficiency
• Robustness
• Resource-constrained Optimization

Innovation

• LoRA Optimization
• Resource-efficient Training
• Enhanced Discriminative Ability