Analysis of a Model Adaptation Strategy

An engineer is adapting a pre-trained language model for a new task. They want to add a small number of trainable vectors to guide the model's behavior without changing any of the original model weights. What is the fundamental architectural difference between a strategy that adds these vectors only to the input embedding layer versus one that adds them to the input of every transformer layer?

Match each parameter-efficient adaptation method to the description of how it modifies a pre-trained model's architecture.