Multiscale POD of Transformer Attention Fields: Scale-Selective Analysis via Morlet Scalogram
Researchers have developed a new method called scale-selective Proper Orthogonal Decomposition (POD) to analyze transformer attention fields, drawing inspiration from fluid dynamics techniques. This approach uses the Morlet continuous wavelet transform to identify dominant temporal scales within attention patterns across a document ensemble. The extracted modes reveal how attention shifts from finer scales in earlier layers to coarser scales in later layers of transformer models. AI
IMPACT Provides a novel analytical framework for understanding internal transformer model dynamics, potentially aiding in interpretability and optimization.