The obvious easy solution would be to teach LLMs to guide the user through their “thinking process” or however you may call it. Instead of answering outright. This is what people do too, right? They look at what they thought and/or wrote. Or they would say “let’s test this”. Like good teachers do. Problem is, that would require some sort of intelligence, which artificial intelligence ironically doesn’t possess.
I would consider even LLMs actual AI. Even bots in video games are called AIs, no? But I agree that people are vastly overestimating their capabilities and I hate the entrepreneurial bullshitting as much as everyone else.
Machine learning! That was the better term.
The very strategy of asking LLMs to “reason” or explain an answer tends to make them more accurate.
Because instead of the first token being “Yes” or “No”, it’s “That depends,” or If we look at…"
Thus increasing the number of tokens that determines the answer from 1, to theoretically hundreds or more.
Those are all one token. A token can be a whole sentence. Tokenization tends to be based on LZW compression which combines common phrases (of any length, e.g. “Once upon a time” could be a single token because it’s recurring)
“Yes” is almost always followed by an explanation of a single idea while “It depends” is followed by several possible explanations.
There are chain of thought and tree of thought approaches and maybe even more. From what I understand it generates answer in several passes and even with smaller models you can get better results.
However it is funny how AI (LLMs) is heavily marketed as a thing that will make many jobs obsolete and/or will take over humanity. Yet to get any meaningful results people start to build whole pipelines around LLMs, probably even using several models for different tasks. I also read a little about retrieval augmented generation (RAG) and apparently it has a lot of caveats in terms of what data can and can not be successfully extracted, data should be chunked to fit into the context and yet retain all the valuable information and this problem does not have “one size fits all” solution.
Overall it feels like someone made black box (LLM), someone tried to use this black box to deal with the existing complexity, failed and started building another layer of complexity around the black box. So ultimately current AI adopters can find themselves with two complex entities at hand. And I find it kind of funny.
