Feed Forward Layer

Feedforward layers are a fundamental component of many neural network architectures, particularly in transformers, serving as crucial processing units that transform data representations. Current research focuses on understanding their role in various tasks, including next-token prediction, speech processing, and code generation, exploring how their internal weights and activation patterns contribute to model performance and interpretability. This research is significant because it helps improve model efficiency, enables techniques like model pruning and editing, and enhances our understanding of how these networks learn and generalize, ultimately leading to more effective and explainable AI systems.