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

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 parameter

Analyzing Model Behavior Under Maximum Likelihood Estimation