Prompt Learning

Prompt Learning converts the discrete, human-authored natural-language prompt into a set of continuously differentiable embedding vectors—soft tokens—prepended to the model's input representation and optimized via GD on a task dataset ^{[113, 121]}. %, occupying the structural boundary between context injection and parametric adaptation. It resembles PE in its input-space operation—prepending sequence tokens to the input—but functions mechanically like FT in its optimization: learnable continuous embeddings (soft tokens) are explicitly trained via backpropagation (Parametric Update).% through the frozen base model. Therefore, the parametric modification is confined entirely to the prepended embedding vectors (Partial); all base model parameters remain frozen. The Ad-hoc Permanent, Scheduled Permanent coordinate means trained soft prompt tensors must be registered as distinct parametric artifacts in the system configuration record: deploying a different soft prompt tensor with the same frozen base model constitutes a parametric modification to the deployed system and requires version control and behavioral regression testing against the task coverage specification defined during original validation.