Layer-dependent dynamic spectral weighting for efficient transformer models

Transformer models have revolutionized artificial intelligence applications in natural language processing, but their computational demands remain significant. We introduce Layer-Dependent Dynamic Spectral Weighting (LD-DSW), a novel approach that selectively modulates attention head contributions based on spectral characteristics at specific transformer layers. Our key finding is that applying this technique primarily to later layers provides the optimal balance between performance and efficiency. Systematic evaluation across diverse datasets shows consistent reductions in validation loss together with higher inference speeds; later-layer selective application offers a favorable efficiency–performance trade-off, whereas full-network application can yield the largest gains on more complex corpora. This targeted approach significantly reduces computational overhead in resource-constrained environments without compromising model capabilities. Beyond immediate efficiency gains, our findings provide critical insights for designing more adaptable transformer architectures, offering broader implications for engineering applications of artificial intelligence.