alt Hacker NewsDescribing it as a "next-token predictor" in the sense that this would mean it's fundamentally limited to just a fraction of an inferential step is doubly wrong:
1. In order to select even the first word of a meaningful sentence, it already has to have structure and meaning of what follows captured somewhere inside, mostly in it's weights/activations or indexed by it's state vector.
2. What you see when you use an LLM is not next-token prediction directly next to the prompt, but instead following a block of varying length of next-token prediction that happened to make progress on the problem in your prompt, and which just summarizes the results.
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Even so, one might wonder why we don't try making systems that take different approaches. For example, after a traditional first pass of output, they could do sliding-window "optimizations" considering each token in the context of tokens both before and after, and possibly replace words or phrases in-place.
For example, I've noticed quite a few cases recently of LLMs outputting "but" where "and" would make more sense, or vice-versa. Surely that could be improved by such an approach?
I think you are just litigating the philosophical debate of Bayesian vs frequentist interpretations of probability. Because the weights really are just probabilities.
Bayesians say that the probabilities represent strength of belief, implying some subjective knowledge or information. It is necessarily subjective in that it requires priors, i.e information the predictor knew before making the prediction. In other words, the LLM has priors from training and is predicting tokens using real knowledge
Frequentists would say that probabilities are simply objective facts - e.g we all agree that the physical property of temperature follows from any molecules matching a particular energy distribution. You’re not predicting anything, there’s just some outcomes that are happening at the expected rate. In other words, the LLM is a stochastic parrot/next token predictor
> Describing it as a "next-token predictor" in the sense that this would mean it's fundamentally limited to just a fraction of an inferential step
I don't think anyone is doing that though; we know LLMs are not simple Markov chains, and that the prediction they make is based on more than the previous X words.
It's not minimising to describe even a complex prediction process as prediction.