this post was submitted on 24 Jul 2024
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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, textbooks, etc?
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".
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".
Eh but LLMs abstract. It has seen " have feathers" and " 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.
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 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 "Feathers only grow on birds and dinosaurs. Cats are mammals. No, cats don't have feathers." And you'd only see the latter bit. It was a pretty neat approach to improving LLM reasoning.