> Currently all LLMs are only determining the most probable next token, but this means they are not aware of the probability of the entire sequence of tokens th

> Currently all LLMs are only determining the most probable next token, but this means they are not aware of the probability of the entire sequence of tokens they are emitting. That is, they can only build sentences by picking the most probable next word, but can never choose the most probable sentence They're normally trained to output a probability distribution for the next token and _sample_ from that distribution. Doing so iteratively, if you work through the conditional probabilities, samples from the distribution of completed prompts (or similarly if you want to stop at a single sentence) with the same distribution as the base training data. You're right that you can't pick the most likely sentence in general, but if there exists a sentence likely enough for you to care then you can just repeat the prompt a few times and take the most common output, adjusting the repetition count in line with your desired probability of failure. Most prompts don't have a "most likely" sentence for you to care about though. If you ask for meal suggestions with some context, you almost certainly want a different response each time, and the thing that matters is that the distribution of those responses is "good." LLMs, by design, can accomplish that so long as the training data has enough information and the task requires at most a small, bounded amount of computation.