Output Selection Formula in a Prefix-Tuned Transformer Layer

Inter-Layer Data Flow in Prefix-Tuning

Consequences of Output Selection in a Modified Transformer

In a Transformer layer adapted for prefix-tuning, the input consists of a set of trainable prefix vectors followed by the hidden states from the original input sequence. After this combined input is processed by the layer, the resulting hidden states corresponding to the prefix vectors are discarded, and only the states for the original sequence are passed on. What is the most critical reason for this selective output process?

In a Transformer architecture modified for prefix-tuning, the hidden state representations corresponding to the trainable prefix vectors are passed along with the main input's hidden states to the subsequent layer to ensure the model has access to the learned task-specific information at every stage.

Inter-Layer Data Flow in Prefix-Tuning

In a deep neural network composed of many layers, the output representation from one layer serves as the complete input for the subsequent layer. What is the most critical consequence of this strictly sequential processing structure?

Data Flow in a Multi-Layer Network

Debugging a Multi-Layer Network