The issue here is that you are describing the goal of LLMs, not how they actually work. The goal of an LLM is to pick the next most likely token. However, it cannot achieve this via rudimentary statistics alone because the model simply does not have enough parameters to memorize which token is more likely to go next in all cases. So yes, the model “builds up statistics of which tokens it sees in which contexts” but it does so by building it’s own internal data structures and organization systems which are complete black boxes.
Also, going “one token at a time” is only a “limitation” because LLMs are not accurate enough. If LLMs were more accurate, then generating “one token at a time” would not be an issue because the LLM would never need to backtrack.
And this limitation only exists because there isn’t much research into LLMs backtracking yet! For example, you could give LLMs a “backspace” token: news.ycombinator.com/item?id=36425375
Have you tried that when it’s correct too? And in that case you mention it has a clean break and then start anew with token generation, allowing it to go a different path. You can see it more clearly experimenting with local LLM’s that have fewer layers to maintain the illusion.
If it’s correct, then it gives a variety of responses. The space token effectively just makes it reflect on the conversation.
We’re trying to make a flying machine by improving pogo sticks. No matter how well you design the pogo stick and the spring, it will not be a flying machine.
To be clear, I do not believe LLMs are the future. But I do believe that they show us that AI research is on the right track.
Building a pogo stick is essential to building a flying machine. By building a pogo stick, you learn so much about physics. Over time, you replace the spring with some gunpowder to get a mortar. You shape the gunpowder into a tube to get a model rocket and discover the pendulum rocket fallacy. And finally, instead of gunpowder, you use liquid fuel and you get a rocket that can go into space.