How does dropout regularization mitigate overfitting in Transformer models?

Dropout regularization mitigates overfitting in Transformer models by randomly setting a fraction of the neurons' outputs to zero during training. This forces the network to learn more robust features that are not dependent on any single neuron. Overfitting occurs when a model learns the training data too well, including its noise and specific details, which leads to poor performance on unseen data. Dropout addresses this by preventing neurons from co-adapting to each other during training. Co-adaptation happens when neurons become overly reliant on the presence of other specific neurons, making the network brittle and less generalizable. By randomly dropping out neurons, dropout forces the remaining neurons to learn more general and independent features that can compensate for the absence of the dropped-out neurons. This effectively trains multiple "thinned" networks within the same model, and the final prediction can be seen as an average of the predictions of these thinned networks. During inference (when the model is used to make predictions on new data), dropout is typically turned off, and the weights of the neurons are scaled by the dropout rate to compensate for the fact that all neurons are active. This ensures that the output of the network is consistent between training and inference. Dropout is commonly applied in the feed-forward networks and attention layers of the Transformer model, as these are the areas where overfitting is most likely to occur. The dropout rate (the fraction of neurons to drop out) is a hyperparameter that is typically set to a value between 0.1 and 0.5.