A language model is being trained to predict the next word. For a given input, the ideal target probability distribution, $Pr^t$, assigns a probability of 0.6 to the word 'sunny' and 0.3 to the word 'warm'. All other words have a combined probability of 0.1.

Two student models, with parameter sets $\theta_A$ and $\theta_B$, produce the following distributions for the same input:

• Model A ($Pr_{\theta_A}^s$): 'sunny' = 0.7, 'warm' = 0.1, others = 0.2
• Model B ($Pr_{\theta_B}^s$): 'sunny' = 0.5, 'warm' = 0.4, others = 0.1

Based on a typical loss function that measures the discrepancy between the entire predicted and target distributions, which model is likely performing better (i.e., would have a lower loss value)? Justify your answer by explaining how the loss function evaluates these distributions.