To refine this a bit more, a LLM is a function that takes an array of integers (or really, a batch of arrays of integers), and returns a probability distribution for each possible integer, with the array shifted left by one place to enable prediction.
The integers are really indeces into the embedding space.
So you'd want to think more that the model maintains a giant matrix (one row = one token; one column is an embedding feature).
The array of indices gets the relevant embeddings to shove through the rest of the model's forward pass.
The API docs talk about letting you specify your own stop token (like "<!-->") but I don't think "token" is meant in the same sense here.
A score like this can be useful for active learning though, where you find areas of low confidence in your dataset and get more data to train on.
I don't understand that, because wouldn't the probabilities later in the sentence be impacted by the tokens chosen earlier in the sentence?
Yes, what happens later in the sentence depends on the particular choice you made earlier in the sentence.
More recently, there are models like RWKV that can run in both parallel (GPT-like) mode for training and serial (RNN-like) mode for inference.
But transformers always output a probability distribution at each position in the context.