Hybrid Fine-Tuning of Large Language Models Using LoRA: Enhancing Multi-Task Text Classification Through Knowledge Sharing

چکیده مقاله

Abstract Background and Objectives: Large Language Models have demonstrated ‎exceptional performance across various NLP tasks, especially when fine-tuned for ‎specific applications.‎‏ ‏Full fine-tuning of large language models requires extensive ‎computational resources, which are often unavailable in real-world settings. While ‎Low-Rank Adaptation (LoRA) has emerged as a promising solution to mitigate these ‎challenges, its potential remains largely untapped in multi-task scenarios. This ‎study addresses this gap by introducing a novel hybrid approach that combines ‎LoRA with an attention-based mechanism, enabling fine-tuning across tasks while ‎facilitating knowledge sharing to improve generalization and efficiency.‎‏ ‏‎ This study ‎aims to address this gap by introducing a novel hybrid fine-tuning approach using ‎LoRA for multi-task text classification, with a focus on inter-task knowledge sharing ‎to enhance overall model performance.‎ Methods: We proposed a hybrid fine-tuning method that utilizes LoRA to fine-tune ‎LLMs across multiple tasks simultaneously. By employing an attention mechanism, ‎this approach integrates outputs from various task-specific models, facilitating ‎cross-task knowledge sharing. The attention layer dynamically prioritizes relevant ‎information from different tasks, enabling the model to benefit from ‎complementary insights. ‎ Results: The hybrid fine-tuning approach demonstrated significant improvements ‎in accuracy across multiple text classification tasks. On different NLP tasks, the ‎model showed superior generalization and precision compared to conventional ‎single-task LoRA fine-tuning. Additionally, the model exhibited better scalability ‎and computational efficiency, as it required fewer resources to achieve comparable ‎or better performance. Cross-task knowledge sharing through the attention ‎mechanism was found to be a critical factor in achieving these performance gains.‎ Conclusion: The proposed hybrid fine-tuning method enhances the accuracy and ‎efficiency of LLMs in multi-task settings by enabling effective knowledge sharing ‎between tasks. This approach offers a scalable and resource-efficient solution for ‎real-world applications requiring multi-task learning, paving the way for more ‎robust and generalized NLP models. ‎