Transformer Layer

Transformer layers are the fundamental building blocks of large language models and other deep learning architectures, with research focusing on improving their efficiency, interpretability, and performance. Current efforts explore architectural modifications like incorporating convolutional layers, employing low-rank approximations and structured pruning for compression, and developing novel training objectives and regularization techniques to enhance model accuracy and reduce computational costs. Understanding the internal workings of these layers, including information flow and the role of individual components (e.g., attention heads, feed-forward networks), is crucial for advancing both theoretical understanding and practical applications of transformer-based models across diverse domains.