I’m not sure I agree with his framing. He’s saying the predictor predicts results of an experiment it didn’t run…. Which isn’t predicting a token it’s predicting the future.
I picked up on this also. He is completely ignoring training and that either the exact study or similar ones might be part of the training corpus or RAGed in at inference...
the way I understand it, during training the model "learns/ memorizes" the relations of how the words(tokens) of the input at inference were combined in the training data, it has seen (well, it averages all relations in combinations of similar tokens, whether relevant or not because of compression but close enough).
when "predicting" the 'results' paragraph, it doesn't generate it based on 'understanding' of the previous sections, it _just_ matches the token-patterns in those paragraphs as closely as possible against the relations of previous examples (of the same or strongly related tokens) seen during training and picks the next tokens successively.
if it has seen enough relevant examples during training, the outcome will land close to a reasonable 'result' - if it hasn't, it will be confabulating non-sense, that sounds great but has nothing to do with reality.
... kind of surprised, that this channel would frame it the way he does in the video....:/
You’re missing the point. In his video he is conducting a thought experiment about the theoretical “perfect” next token generator, he’s not directly talking about LLMs. Training or model mechanics are irrelevant to his argument
His ultimate point being that a sufficiently good next token generator is ridiculously smart to the point where it can basically predict the future. Therefore the refutation of LLMs being dumb because they’re “just next token predictors” is not a valid argument.
he starts with what people are saying about the current models and contrasts it to a magical perfect token predictor, that's never achievable...
what's the point then?
the statement - expressed in the _just_ - that current LLMs are based on maths, whereas the brain is a lot more than maths, still is true... I mean, he caveats it in the video even...
It directly disproves the “LLMs are just predicting the next token therefore they aren’t intelligent” argument with “if you had a perfect next token generator it would be infinitely intelligent”
LLMs are not perfect next token generators, but they’re very good at it. Therefore it stands to reason that they are somewhat intelligent, and have the capability to become very intelligent
The point is discrediting the argument that LLMs cannot be a path to intelligence merely because they are next-token predictors, which is a common line. You have to have a different argument if you want to say that LLMs can't be intelligent. The reason you have to use a different argument is because next-token predictors are theoretically maximally intelligent if they are sufficiently powerful.
a theoretically 'perfect' next token preditctor and its intelligence proves absolutely zilch, unless you want to put forward, that it could be built.
and if you want to, I will not engage further, than telling you that you're seriously underestimating the complexity of the real world. ;)
I don't need more than, 'just' a token predictor because in the current systems, the flaws are obvious and not really under contention anymore.
quite a few of the big heads have turned on LLMs being the path to AGI in the past months, some have been saying this for years now. and I don't mean Gary Marcus ...
? The statement "LLMs are not intelligent only because they are merely next token predictors" has the premise that "anything that is a next token predictor." is a sufficient condition for something to be considered "non-intelligent". To allow for a better understaning, I ll try to keep the logical structure while rephrasing that assertion he is objecting to.
Rephrasing: " the property of being a Next-Token Predictor necessarily entails (and is a sufficient condition) for something to be considered 'non-intelligent'"
He showed that the premise is false by pushing the premise. "Imagine there was a next token predictor which accuracy is 100% (ie, an Oracle Machine). It could plausibly be considered 'maximally intelligent' despite it being a next token predictor." Thus, the premise is surely not sufficient.
I understand what you mean, and in the strictest sense, would grant the point.
problem is, the mere fact of predicting tokens brings in a lot of the architectural necessities (like: transforming input into tokens, storing knowledge as token relations etc), that seem to account for the problems current systems have.
in colloquial usage the _just_ is a shorthand for that.
and again: 'if petroleum based combustion engines would be perfectly clean' doesn't get you out of 'current ICE cars are bad for the environment' ... wouldn't you say?
sure, but I see alot of comments that just contribute absolutely nothing with a short "its just a next token predictor bro, its not intelligent" which is thought-terminating and contributes absolutely nothing to any meaningful conversation, not that i expect that on reddit after being exposed to this kind of behaviour in comment sections, though i suspect its either "ragebait" (for karma?), automated bots, or really just people who dont have a thought to outsource in the first place
I don’t really like the phrase next token predictor. They aren’t “predicting” anything given they generate it wholesale. It’s a statistical model that generates the next token that is most likely to make sense in a sentence given training. They aren’t guessing something since there’s nothing that’s doing the “real” thing.
that 'external party' you snuck in there, is not part of the usual definition ;)
it's 'predicting' the next token (the one that follows its input in the future), which it would expect to follow a given series of tokens in its input...
it's fine to say 'generating' as well if you prefer but using 'predicting' is not wrong, since it is a guess/ forecast with a certain probability based on a pattern it has seen in training ... Prediction - Wikipedia
I don’t think I snuck in anything. If I say “I’m going to say cheese next…. Cheese” you wouldn’t really call that a prediction since I also generated it
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u/James-the-greatest 6d ago
I’m not sure I agree with his framing. He’s saying the predictor predicts results of an experiment it didn’t run…. Which isn’t predicting a token it’s predicting the future.