Today’s language models are more sophisticated than ever, but they still struggle with the concept of negation. That’s unlikely to change anytime soon.
It must have some internal models of some things, or else it wouldn’t be possible to consistently make coherent and mostly reasonable statements. But the fact that it has a reasonable model of things like grammar and conversation doesn’t imply that it has a good model of literally anything else, which is unlike a human for whom a basic set of cognitive skills is presumably transferable. Still, the success of LLMs in their actual language-modeling objective is a promising indication that it’s feasible for a ML model to learn complex abstractions.
Yes, but that’s not how LLMs work. My statement depends heavily on the fact that a LLM like GPT is coaxed into coherence by unsupervised or semi-supervised training. That the training process works is the evidence of an internal model (of language/related concepts), not just the fact that something outputs coherent statements.
if I have a bot pick a random book and copy the first sentence into my clipboard, my clipboard becomes capable of consistently making coherent statements. unsupervised training 👍
this isn’t necessarily true. patterns in data aren’t by nature proof of an underlying system of logic. if you run the line-fitting machine on any kind of data, its going to output a line. considering just how much data is encoded into these transformers, i don’t think we can conclusively say that it has a underlying conception of how language works, much less an understanding of the concepts that language represents. it could really just be using the vast quantities of data it has to output approximately correct statements. there’s absolutely structure there, but it doesn’t have to have the kind of structured understanding humans have about language to produce language, in the same way a less sophisticated machine learning model doesn’t have to know what kind of data its fitting a line to to make a line.
It must have some internal models of some things, or else it wouldn’t be possible to consistently make coherent and mostly reasonable statements. But the fact that it has a reasonable model of things like grammar and conversation doesn’t imply that it has a good model of literally anything else, which is unlike a human for whom a basic set of cognitive skills is presumably transferable. Still, the success of LLMs in their actual language-modeling objective is a promising indication that it’s feasible for a ML model to learn complex abstractions.
if I copy a coherent sentence into my clipboard, my clipboard becomes capable of consistently making coherent statements
Yes, but that’s not how LLMs work. My statement depends heavily on the fact that a LLM like GPT is coaxed into coherence by unsupervised or semi-supervised training. That the training process works is the evidence of an internal model (of language/related concepts), not just the fact that something outputs coherent statements.
if I have a bot pick a random book and copy the first sentence into my clipboard, my clipboard becomes capable of consistently making coherent statements. unsupervised training 👍
let me free up some of your time so you can go figure out how LLMs actually work
Did you forget to actually ban me? I dunno why you were going to, or why you think I don’t know how LLMs work, but that’s your business.
I don’t think they know how lemmy works, let alone LLMs xD
Eh?
this isn’t necessarily true. patterns in data aren’t by nature proof of an underlying system of logic. if you run the line-fitting machine on any kind of data, its going to output a line. considering just how much data is encoded into these transformers, i don’t think we can conclusively say that it has a underlying conception of how language works, much less an understanding of the concepts that language represents. it could really just be using the vast quantities of data it has to output approximately correct statements. there’s absolutely structure there, but it doesn’t have to have the kind of structured understanding humans have about language to produce language, in the same way a less sophisticated machine learning model doesn’t have to know what kind of data its fitting a line to to make a line.
it doesn’t. that’s why we’re calling it “spicy autocompletion” .
It does, which is why it’s autocompletion and not auto-gibberish.
Talk about begging the question