In a machine learning process where a smaller model is trained to replicate the behavior of a larger, pre-trained model, a specific loss function is used. This function is expressed as: $Loss(\text{Pr}^t(\cdot|\cdot), \text{Pr}_{\theta}^s(\cdot|\cdot), \mathbf{x})$. Analyze this expression by breaking it down into its three primary components. For each component—$\text{Pr}^t(\cdot|\cdot)$, $\text{Pr}_{\theta}^s(\cdot|\cdot)$, and $\mathbf{x}$—explain its specific role and significance within the training objective.