LLMs (current implementation) are probabilistic so it really needs the actual code to predict the most likely next tokens. Now loading the whole code base can be a problem in itself, since other files may negatively affect the next token.
My impression is that LLMs predict the next token based on the prior context. They do that by having learned a probability distribution from tokens -> next-token.
Then as I understand, the models are never reasoning about the problem, but always about what the next token should be given the context.
The chain of thought is just rewarding them so that the next token isn't predicting the token of the final answer directly, but instead predicting the token of the reasoning to the solution.
Since human language in the dataset contains text that describes many concepts and offers many solutions to problems. It turns out that predicting the text that describes the solution to a problem often ends up being the correct solution to the problem. That this was true was kind of a lucky accident and is where all the "intelligence" comes from.
I don't mean that the textual prediction is simple, it's very advanced and it learns all kinds of relationships, patterns and so on.
But it doesn't have a real model and thinking process relating to the the actual problem. It thinks about what text could describe a solution that is linguistically and language semantically probable.
Since human language embedds so many of the logics and ground truths that's good enough to result in a textual description that approximate or nails the actual underlying problem.
And this is why we see them being able to solve quite advanced problems.
I admit that people are wondering now, what's different about human thinking? Maybe we do the same, you invent a probable sounding answer and then check if it was correct, rinse and repeat until you find one that works.
But this in itself is a big conjecture. We don't really know how human thinking works. We've found a method that works well for computers and now we wonder if maybe we're just the same but scaled even higher or with slight modifications.
I've heard from ML experts though that they don't think so. Most seem to believe different architecture will be needed, world models, model ensembles with various specialized models with different architecture working together, etc. That LLMs fundamentaly are kind of limited by their nature as next token predictors.
How you find the function that does the mapping probably doesn't matter. We use probability theory and information theory, because they're the best tools for the job, but there's nothing to say you couldn't handcraft it from scratch if you were some transcendent creature.
The text of human natural language that it is trained on encodes the solutions to many problems as well as a lot of ground truths.
The way I think of it is. First you have a random text generator. This generative "model" in theory can find the solution to all problems that text can describe.
If you had a way to assert if it found the correct solution, you could run it and eventually it would generate the text that describes the working solution.
Obviously inefficient and not practical.
What if you made it so it skipped generating all text that aren't valid sensical English?
Well now it would find the correct solution in way less iterations, but still too slow.
What if it generated only text that made sense to follow the context of the question?
Now you might start to see it 100-shot, 10-shot, maybe even 1-shot some problems.
What if you tuned that to the max? Well you get our current crop of LLMs.
What else can you do to make it better?
Tune the dataset, remove text that describe wrong answers to prior context so it learns not to generate those. Add more quality answers to prior context, add more problems/solutions, etc.
Instead of generating the answer to a mathematical equation the above way, generate the Python code to run to get the answer.
Instead of generating the answer to questions about current real world events/facts (like the weather). Have it generate the web search query to find it.
If you're asking a more complex question, instead of generating the answer directly, have it generate smaller logical steps towards the answer.
Etc.
Regardless if you believe LLMs are probabilistic or not, I think what we are both saying is context is king and what it (LLM) says is dictated by the context (either through training) or introduced by the user.
That's the level to look at, unless you have a dualist view of the brain (we are channeling a super-natural forces).
Complexity theory doesn’t have a mathematics (yet), but that doesn’t mean we can’t see that it exists. Studying the brain at the lowest levels haven’t lead to any major insights in how cognition functions.
Is it entirely probabilistic? I don't think so. But, it does seem that a chunk of our speech generation and processing is similar to LLMs. (e.g. given the words I've heard so far, my brain is guessing words x y z should come next.)
I feel like the conscious, executive mind humans have exercises some active control over our underlying probabilistic element. And LLMs lack the conscious executive.
e.g. They have our probabilistic capabilities, without some additional governing layer that humans have.
If you know what a function achieves, and you trust it to do that, you don’t need to see/hold its exact implementation in your head.