Fine-Tuning Objective as Log-Likelihood Maximization

Training Objective as Joint Log-Likelihood Maximization of Concatenated Sequences

A machine learning engineer is fine-tuning a pre-trained language model on a specialized dataset of question-answer pairs. The chosen training objective is to adjust the model's parameters to maximize the sum of the log-probabilities of the ground-truth answers, conditioned on their corresponding questions. Which statement best analyzes the direct effect of this training objective on the model's behavior?

Interpreting Fine-Tuning Loss

Analyzing Fine-Tuning Behavior

When fine-tuning a language model, the objective of maximizing the sum of the log-likelihoods of the true responses given the prompts is mathematically equivalent to minimizing the mean squared error loss over the dataset.