A language model is trained on a dataset $D$ by finding the parameters $\hat{\theta}$ that optimize the following objective: $\hat{\theta} = \arg \min_{\theta} \sum_{\mathbf{x} \in D} \text{Loss}_{\theta}(\mathbf{x})$ Which statement best analyzes the relationship between this optimization objective and the principle of Maximum Likelihood Estimation (MLE)?

Parameter Selection via Loss Minimization

Deconstructing the Training Objective