Investigating the Synergistic Effects of Dropout and Residual Connections on Language Model Training

Residual connections support smoother model training of deeper This paper examines the pivotal role of dropout techniques in mitigating networks by adding a layer's output to that of subsequent layers.[2] overfitting in language model training. It conducts a comprehensive This study explores the impact of varying dropout rates and investigation into the influence of variable dropout rates on residual connections in the Transformer architecture for language both individual layers and residual connections within the context modeling. Using a decoder trained on the classic literature, we of language modeling. Our study conducts training of a decoder implementation analyze how these architectural adjustments affect training convergence, on the classic Tiny Shakespeare data to examine the validation errors, and generalizability.