A language model is being trained on a text corpus where it learns to predict the next word in a sequence. The model's entire vocabulary is ordered as follows: ['a', 'bright', 'day', 'is', 'shining']. If the model is given the input context 'a bright' and the actual next word in the training data is 'day', which vector correctly represents the ground-truth target for this specific training step?

In the context of training a language model, representing the ground-truth distribution as a one-hot vector implies that the training process considers all incorrect tokens to be equally wrong, regardless of their semantic similarity to the correct token.

Explaining the Ground-Truth Vector