A language model is being trained to predict the next word in a sequence. For a given training example, the function $\mathcal{L}(\mathbf{p}^{\theta}, \mathbf{p}^{\text{gold}})$ is used to measure the difference between the model's predicted probability distribution over the vocabulary ($\mathbf{p}^{\theta}$) and the true distribution ($\mathbf{p}^{\text{gold}}$), where the true next word has a probability of 1. If the calculated value of $\mathcal{L}$ is very high for this example, what does this most accurately indicate?

Evaluating Model Performance via Loss

Comparing Model Predictions via Loss