hybrid fine-tuning of large language models using lora: enhancing multi-task text classification through knowledge sharing

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. ‎