A language model is being fine-tuned on a dataset of instruction-response pairs. Consider the following training example:

• Input: What is the capital of France?
• Correct Response: Paris

The model processes the input and must predict the first token of the response. Below are two potential probability distributions (States A and B) that the model could generate for this first token at different points during training.

• State A: {'Paris': 0.15, 'London': 0.10, 'The': 0.08, ...}
• State B: {'Paris': 0.25, 'London': 0.05, 'The': 0.04, ...}

Based on the standard objective for this type of training, which statement provides the most accurate analysis?

A language model is being fine-tuned on a dataset. For the input Translate to French: I love to learn., the correct starting token for the response is J'. At a particular step in training, the model produces the following probabilities for the first token:

• Je: 0.35
• J': 0.25
• Le: 0.15
• Mon: 0.10
• (all other tokens): 0.15

Given that the training objective is to maximize the likelihood of the correct sequence, how will the training process adjust the model's parameters in the next immediate step for this specific token prediction?

Analyzing Model Behavior Under Maximum Likelihood Estimation