That’s what smaller models do, but it doesn’t yield great performance because there’s only so much stuff available. To get to gpt4 levels you need a lot more data, and to break the next glass ceiling you’ll need even more.
Comment on AI trained on AI garbage spits out AI garbage.
KevonLooney@lemm.ee 3 months ago
provenance requires some way to filter the internet into human-generated and AI-generated content, which hasn’t been cracked yet
It doesn’t need to be filtered into human / AI content. It needs to be filtered into good (true) / bad (false) content. Or a “truth score” for each.
We don’t teach children to read by just handing them random tweets. We give them books that are made specifically for children. Our filtering mechanism for good / bad content is very robust for humans. Why can’t AI just read every piece of “classic literature”, famous speeches, popular books, good TV and movie scripts, etc?
Zos_Kia@lemmynsfw.com 3 months ago
KevonLooney@lemm.ee 3 months ago
Then these models are stupid. Humans don’t start as a blank slate. They have an inherent aptitude for language and communication. These models should start out with basics of language, so they don’t have to learn it from the ground up. That’s the next step. Right now they’re just well read idiots.
Zos_Kia@lemmynsfw.com 3 months ago
Then these models are stupid
Yup that is kind of the point. They are math functions designed to approximate human tasks.
These models should start out with basics of language, so they don’t have to learn it from the ground up. That’s the next step. Right now they’re just well read idiots.
I’m not sure what you’re pointing at here. How they do it right now, simplified, is you have a small model designed to cut text into tokens (“knowledge of syllables”), which are fed into a larger model which turns tokens into semantic information (“knowledge of language”), which is fed to a ridiculously fat model which “accomplishes the task” (“knowledge of things”).
The first two models are small enough that they can be trained on the kind of data you describe, classic books, movie scripts etc… A couple hundred billion words maybe. But the last one requires orders of magnitude more data, in the trillions.
lvxferre@mander.xyz 3 months ago
That isn’t enough because the model isn’t able to reason.
I’ll give you an example. Suppose that you feed the model with both sentences:
Both sentences are true. And based on vocabulary of both, the model can output the following sentences:
Both are false but the model doesn’t “know” it. All that it knows is that “have” is allowed to go after both “cats” and “birds”, and that both “feathers” and “fur” are allowed to go after “have”.
KevonLooney@lemm.ee 3 months ago
It’s not just a predictive text program. That’s been around for decades. That’s a common misconception.
As I understand it, it uses statistics from the whole text to create new text. It would be very rare to output “cats have feathers” because that phrase doesn’t ever appear in the training data. Both words “have feathers” never follow “cats”.
skulblaka@sh.itjust.works 3 months ago
But the fact remains that it doesn’t know what a cat or a feather is. All of this is still based purely on statistical frequency and not at all on actual meanings.
vrighter@discuss.tchncs.de 3 months ago
and that is exactly how a predictive text algorithm works.
some tokens go in
they are processed by a deterministic, static statistical model, and a set of probabilities (always the same, deterministic, remember?) comes out.
pick the word with the highest probability, add it to your initial string and start over.
if you want variety, add some randomness and don’t just always pick the most probable next token.
Coincidentally, this is exactly how llms work. It’s a big markov chain, but with a novel lossy compression algorithm on its state transition table.
CeeBee_Eh@lemmy.world 3 months ago
Everyone who says this doesn’t actually understand how LLMs work.
Multivector word embeddings create emergent relationships that’s new knowledge that doesn’t exist in the training dataset.
Computerphile did a good video on this well before the LLM craze.
barsoap@lemm.ee 3 months ago
Eh but LLMs abstract. It has seen “<animal> have feathers” and “<animal> have fur” quite a lot of times. The problem isn’t that LLMs can’t reason at all, the problem is that they do employ techniques used in proper reasoning, in particular tracking context throughout the text (cross-attention) but lack techniques necessary for the whole thing, instead relying on confabulation to sound convincing regardless of the BS they spout. Suffices to emulate an Etonian, but that’s not a high standard.
FaceDeer@fedia.io 3 months ago
Workarounds for those sorts of limitations have been developed, though. Chain-of-thought prompting has been around for a while now, and I recall recently seeing an article about a model that had that built right into it; it had been trained to use <thought></thought> tags to enclose invisible chunks of its output that would be hidden from the end user but would be used by the AI to work its way through a problem. So if you asked it whether cats had feathers it might respond "<thought>Feathers only grow on birds and dinosaurs. Cats are mammals.</thought> No, cats don't have feathers." And you'd only see the latter bit. It was a pretty neat approach to improving LLM reasoning.
WalnutLum@lemmy.ml 3 months ago
This isn’t really accurate either. At the moment of generation, an LLM only has context for the input string and the network of text tokens it’s been assigned. It pulls from a “pool” of these tokens based on what it’s already output and the input context, nothing more.
Most LLMs have what are called “Top P”, “Top K” etc, these are the number of tokens that it needs up selecting from based on the previous token, alongside the input tokens. It then randomly chooses one based on temperature settings.
It’s why if you turn these models’ temperature settings really high they output pure nonsense both conceptually and grammatically, because the tenuous thread linking the previous token’s context to the next token has been widened enough that it completely loses any semblance of cohesiveness.
lvxferre@mander.xyz 3 months ago
Your “ackshyually” is missing the point.
CeeBee_Eh@lemmy.world 3 months ago
This is not how the models are trained or work.
Demonstrably false. This isn’t how LLMs are trained or built.
Just considering the contextual relationships between word embeddings that are created during training is evidence enough. Those relationships from the multi-vector fields are an emergent property that doesn’t exist in the dataset.
If you want a better understanding of what I just said, take a look at this Computerphile video from four years ago. And this came out before the LLM hype and before ChatGPT 3, which was the big leap in LLMs.