Hybrid Fine-Tuning of Large Language Models Using LoRA: Enhancing Multi-Task Text Classification Through Knowledge Sharing
- سال انتشار: 1404
- محل انتشار: مجله نوآوری های مهندسی برق و کامپیوتر، دوره: 13، شماره: 2
- کد COI اختصاصی: JR_JECEI-13-2_014
- زبان مقاله: انگلیسی
- تعداد مشاهده: 40
نویسندگان
Department of Computer Engineering, University of Kashan, Kashan, Iran.
Department of Computer Engineering, University of Kashan, Kashan, Iran.
Department of Computer Engineering, University of Kashan, Kashan, Iran.
چکیده
kground 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. کلیدواژه ها
Large Language Model, Fine-Tuning, LoRA, Knowledge Sharing, Attention Mechanismاطلاعات بیشتر در مورد COI
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