Comment by giardini
14 hours ago
You say "...because you actually need massive amounts of data to capture the nuance that language requires..." but humans don't require that! So why do LLMs?
How many books did you read in your education? Far fewer that what LLMs read, no doubt. Of course we "have a life" and so we interact with hundreds of people, which is also a form of learning.
But in any case there's are fundamental differences here between these two systems (LLMs and humans) that we have not yet discerned clearly.
Humans don’t need that? How many words have you taken in from all possible sources by the time you’re 18? And many of those words came with additional data due to tone of voice, inflection and context. And then some of those (in-person, filmed art) came with incredibly subtle data from body language and facial expressions. I think we take in a massive corpus of data every year. Think just of any book you re-read: why did you do it? Did you glean additional insight?
Far fewer than an LLM but then I “know” far less than an LLM. Sam Altman talked about a PhD level expert on any topic; if we expect a SOTA model to answer any question on any topic in a library, presumably that requires a library worth of books go into it?
Say I ask it who are the most beloved Indian cricketers and why, it might not be correct but it will have the right vocabulary - cricket terms and gameplay I’ve not heard of, player names and styles I don’t recognise, things critics and commentators like and dislike about game strategies. And it is like that for almost any topic.
If they hallucinate a Python function, it will look like a plausible function e.g. “hn.create_user_account()”. They don’t hallucinate like
It’s a terrific amount of facts to be able to write even plausibly about tons of topics.
We don't need LLMs that can "answer any question on any topic in a library"!
We don't even need machines that can answer any question in a specific topic area. But we could use machines that can answer "I don't know." when they cannot compute the answer.
Consider the possibility of much-smaller LLMs that answer questions only in limited domains (physics, chemistry, math, medicine, etc.)?
Could not such LLMs exist? If not, why not? Would they not likely be more useful and far, far easier and cheaper to create and run (possibly even with far less hardware) than the current "know-all" LLMs? Also might they possibly avoid certain problems ( e.g., hallucinations, deceit, apparently undesirable behavior, etc.) associated with the "know-all" LLMs we now have?
[flagged]
We need to up the ante, we all know it's the Mumbai Indians. ;)
One can train a decent model at home, for a specific task but you are absolutely correct that in order to have a model that's an expert in any topic it requires training and data on all topics.
I nearly spit out coffee reading this. Amazing use of vocabulary; especially "... oh great spirit of the internet".
I sometimes jokingly explain to certain people not immersed in tech that sometimes saying the "magic words" (commands) from the "codex" (script) in the right sequence with a lot of hand waving allow "magic" (applications/web apps/etc.) to happen.
It generally brightens a patients day and makes things relatable to those non-technical, children, the elderly, or the like.
TLDR: thanks for the chuckle.
I think it's more accurate to say that the fundamental differences (between LLM's and humans) are abundantly clear and what we haven't really got a handle on are the similarities. Why is it that an LLM's can so convincingly mimic some things that humans do? Of course the fact that we don't really understand how humans do it in the first place is a real barrier. Perhaps looking at the intersection will shed some light on how humans work.