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This work proposes Memory Attention (MA), which forms values by combining layer-specific token memory with contextual keys, which supplies token-specific representations, while the keys preserve context dependence at inference.
Normalized Low-Rank Adaptation (NoRA) is introduced, a simple yet effective method that normalizes the down-projection matrices during training, improving standard LoRA without requiring repeated normalization throughout training.
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