The paper proposes Low-Rank Adaptation (LoRA), which freezes pre-trained model weights and injects trainable rank decomposition matrices into each Transformer layer, and states that this greatly reduces the number of trainable parameters for downstream tasks.
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LoRA: Low-Rank Adaptation of Large Language Models → states its contribution → The paper proposes Low-Rank Adaptation (LoRA), which freezes pre-trained model weights and injects trainable rank decomposition matrices into each Transformer layer, and states that this greatly reduces the number of trainable parameters for downstream tasks.
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