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
You can improve the novel writing by using agents. First you generate just an outline with the plot points to every chapter. Then you chop that up and feed it to several agents to flesh out individual chapters. Finally the generated chapters are verified against the outline and overall plot. If that doesn’t fit, the agents are tasked with a rewrite. Repeat that until you have something serviceable.
As you point out, there exists plenty of bad writing in TV series. These often have a number of different authors, who don’t necessarily know the other episodes very well.
I will say that while most of these models are non-deterministic their training data was very similar so if you did something like this I can guarantee you if you churned out enough you would start to see the common threads.
Sure. Lots of fiction, especially TV stick to well established tropes, regardless of a human writing it or not.