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The document introduces Chain of LoRA (COLA), an iterative optimization framework designed to enhance the efficiency of fine-tuning large language models by bridging the gap between low-rank adaptation (LoRA) and full parameter fine-tuning. COLA employs a residual learning approach to merge learned LoRA modules into the pre-trained model without incurring extra computational costs, demonstrating improved generalization error across various tasks. Empirical results show that COLA consistently outperforms tra

Author

princetb305

Language

EN