[mqus]

Well, no. The article pretty much says that any arbitrary statement can be mapped to {true, false, I don't know}. This is still not 100% accurate, but at least something that seems reachable. The model should just be able to tell unknowns, not be able to verify every single fact.

[gary_0]

Determining a statement's truth (or if it's outside the system's knowledge) is an old problem in machine intelligence, with whole subfields like knowledge graphs and such, and it's NOT a problem LLMs were originally meant to address at all.

LLMs are text generators that are very good at writing a book report based on a prompt and the patterns learned from the training corpus, but it's an entirely separate problem to go through that book report statement by statement and determine if each one is true/false/unknown. And that problem is one that the AI field has already spent 60 years on, so there's a lot of hubris in assuming you can just solve that and bolt it onto the side of GPT-5 by next quarter.

[red75prime]

> And that problem is one that the AI field has already spent 60 years on

I hope you don't think that the solutions will be a closed-form expression. The solution should involve exploration and learning. The things that LLMs are instrumental in, you know.

[sirwhinesalot]

Not the same person but I think the "structure" of what the ML model is learning can have a substantial impact, specially if it then builds on that to produce further output.

Learning to guess the next token is very different from learning to map text to a hypervector representing a graph of concepts. This can be witnessed in image classification tasks involving overlapping objects where the output must describe their relative positioning. Vector-symbolic models perform substantially better than more "brute-force" neural nets of equivalent size.

But this is still different from hardcoding a knowledge graph or using closed-form expressions.

Human intelligence relies on very similar neural structures to those we use for movement. Reference frames are both how we navigate the world and also how we think. There's no reason to limit ourselves to next token prediction. It works great because it's easy to set up with the training data we have, but it's otherwise a very "dumb" way to go about it.

[red75prime]

I mostly agree. But, next token prediction is a pretraining phase of an LLM, not all there is to LLMs.

[gary_0]

Of course not, expert systems were abandoned decades ago for good reason. But LLMs are only one kind of ANN. Unfortunately, when all you have is a hammer...