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?

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  • KindnessisPunk@piefed.caEnglish
    2·
    1 day ago

    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.