Attention Normalizes the Wrong Norm (convergentthinking.sh)

Recent insights into attention mechanisms have revealed that the conventional use of softmax normalization may be inadequate, especially as sequence lengths increase. Current models rely on L1 normalization, which can lead to output variance collapse when dealing with longer context sequences, compelling models to learn position-specific adaptations that ultimately do not generalize well to unseen lengths. Researchers propose shifting to L2 normalization, which preserves variance across varying sequence lengths and offers a more robust solution for attention outputs. This shift is significant for the AI/ML community as it can enhance the generalization capabilities of models in handling longer sequences, ultimately leading to improved performance in various natural language processing tasks. By implementing a new formulation—termed p-softmax, which allows for normalization to both L1 and L2 norms—the potential for both accurate training and robust evaluation across diverse sequence lengths is unlocked. The findings indicate that L2 normalization not only adapts to changes in context length more effectively than L1 but also holds promise for other applications in AI where attention mechanisms are employed.