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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  • Endmaker@ani.socialEnglish
    2·
    1 day ago

    Disclaimer: I am honestly a layman in this field. I may get a bunch of stuff wrong, but am happy to learn from experts. Feel free to point mistakes out and destroy me in the replies.

    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.

    This feels like an oversimplification. Unfortunately, I can’t think of a good analogy without anthromorphosising LLMs.

    IMO this anime scene works well enough as an analogy at a super high level: anime_irl

    “Comprehending what other people is saying is one step” - encoder

    “Thinking about how to answer is one more step” - working with the feature representation

    “Putting the things that popped into my mind into words is another step” - decoder

    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?

    By current LLMs, I am going to assume that you are not referring to the raw models, but platforms like ChatGPT, Perplexity, etc with UIs for you to interact with the underlying models.

    There are fundamentally two different problems here: searching the web for answers, and putting the answers into words.

    Like at times it reads through say 10 URLs and generate results, how are they able to achieve this?

    If I ask you: “What is the colour of fire engines?”, I imagine you would answer “Red”, sometimes “Yellow”, off the top of your head.

    What if I ask you “What are the 10 longest rivers in the world”? I believe you won’t be able to give me an answer right away. What you can do is a web search, find the answer, then present the results to me. You can give it to me in 10 short bullets points, or you can come up with an essay with paragraphs describing each river.

    You probably got my point by now, but to make it explicit: finding an answer and putting it into words are two different processes. They are independent of each other, so the final text output can be as long or as short as need be.

    For these LLM platforms, when the model “doesn’t know” the answer, they probably have a subroutine that searches the web, then feed the answer to the underlying model. The model then packages the search results into readable form - in words instead of vectors - to you.

    What’s the engineering behind generating such huge verbose of texts?

    Sorry but I can’t think of a good answer to this at the moment; leaving it to others for now - unless I managed to think of something good.

    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?

    Same for this question.

    Hope the partial answer helps; tried my best to ELI5.