A machine learning engineer is designing a neural network for a complex language task and decides to significantly increase the dimensionality of the vectors that are processed within the network's internal sub-layers. What is the most direct trade-off the engineer should expect from this change?

Impact of Hidden Size on Sub-Layer Dimensions

In a standard Transformer model's architecture, various components have specific dimensionalities defined by key hyperparameters. Match each component listed below with its correct dimensionality, using the following notation: $d$ represents the hidden size, $d_{ffn}$ is the size of the feed-forward network's inner layer, and $n_{head}$ is the number of attention heads.