I have some data science background, and I kinda understand how LLM parameter tuning works and how model generates text.
Simplifying and phrasing my understanding, an LLM works like - Given a prompt: Write a program to check if input is an odd number (converts the prompt to embedding), then the LLM plays a dice game/probability game of: given prompt, then generate a set of new tokens.
Now my question is, how are the current LLM’s are able to parse through a bunch of search results and play the above dice game? Like at times it reads through say 10 URLs and generate results, how are they able to achieve this? What’s the engineering behind generating such huge verbose of texts? Cause I always argue about the theoretical limitations of LLM, but now that these “agents” are able to manage huge verbose of text I dont seem to have a good argument. So what exactly is happening? And what is the limit of AI non theortical limit of AI?
Edit
It’s fundamentally not the same thing as autocomplete. Give autocomplete all the data an LLM has, every gig, every terabyte if it, and it still won’t be an LLM. Autocomplete lacks the semantic meaning layer as well as some other parts. People say it’s nothing but autocomplete from a misunderstanding of what a reward function does in backpropagation training (saying “the reward function is to predict the next word” is not even close to the equivalent of “it’s doing the same thing as autocomplete”)
I’m writing this short reply with hopes that when I have more time in the next two days or so I’ll come back with a more complete explanation, (including why context windows have to be limited).