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I'm not going to stop describing things accurately because someone generated an article that continually undermines its own main point. Limiting the way we talk and think about LLMs to a very narrow set of terms doesn't help us. EDIT: gentler phrasing
I disagree that this accurately describes TFA.
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Post train a model, you'll be able to determine it is not.
The blog post itself says one thing, but then demonstrates the exact thing they’re arguing against. If you can’t grasp that logic gap then there’s no point discussing further.
No logic required, you can just build an LLM yourself, including post training. You'll see that predicting the next token isn't something the model does or is optimized for in RLHF or RLVR. You can hand wave all you like, but you have never done it.
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You are not wrong, but I think atleastoptimal's suggestion was that descriptions like "next-token predictor" are often used to imply that there's not much to see here, perhaps with an implied "obviously" in there. "Stochastic parrot" seems to be a case in point: no-one expects intelligent, informed conversation or working software from a parrot - not even the eternally-resting Alex.
I don't that's a fair description of either 'next-token predictor' or 'stochastic parrot'. Both of those terms describe mechanism, not value--the fact that people squawk that the terms are minimising is projection on their part, not inherent to the phrase.
I'm not going to stop describing things accurately because someone generated an article that continually undermines its own main point. Limiting the way we talk and think about LLMs to a very narrow set of terms doesn't help us. EDIT: gentler phrasing
> generated an article that continually undermines its own main point. I disagree that this accurately describes TFA.
The article is littered with both AI tells and admissions that 'next token prediction' is what is happening. Hence my description.
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 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 LL…
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?
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The word "predict" has a meaning. I don't "predict" my next move in chess. I might predict what someone elses first move is.
The LLM does not determine the next token. It generate odds for all of the tokens it knows as to their likelihood of being 'next'. It's up to the harness running the LLM (and in most cases the a temperature setting) to actually decide on a particular next token. I think it's more accurate to call the thing the LLM actually generates (an ensemble of probabilities) a 'prediction'. It might be accurate to say the harnes…
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You're misinterpreting these articles. Autoregressive LLMs generate tokens one at a time, disputing this is just plain wrong. What is true, however, is that in order to generate the next token autoregressive LLMs produce internal/hidden state about future tokens far past the next token so that it's not like the entire machinery of the algorithm deprives itself of representing where the sentence/text is headed. So "em…
I think the snag is that people use "Next token predictor" as a dismissive statement. In reality it's about as functionally dismissive as "humans only communicate one word at a time".
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> generated an article that continually undermines its own main point. I disagree that this accurately describes TFA.
I'm not sure what you want me to do with that information; clearly I do think that my description is accurate. The article is littered with both AI tells and admissions that 'next token prediction' is what is happening. Hence my description.
For example, you could cite specific things that you believe to be "AI tells" or "admissions".
I think the author is arguing against the idea of a next-token predictor as something that simply uses the weights in the neural net which record the probabilities of tokens following other tokens as a valid definition of what an LLM is. Essentially a massive extreme markov chain. With reinforcement learning and probably attention and other tricks that affect the weights based on things that aren't strictly in the tr…
There’s the mechanical, inference time, autoregressive, one-token-after-another side, which I’m not going to argue isn’t prediction. I just think that’s a relatively uninteresting use of the word “prediction,” because it’s effectively a system predicting its own output.
The more interesting question is what happens at training time. As you describe, reinforcement learning allows the model to learn to output things that it never could have learned simply by predicting what appears in the training corpus.
More concretely, in reinforcement learning there are no ground-truth next tokens to predict.
In supervised machine learning, “prediction” usually means there is some ground-truth label that will eventually be revealed. The model predicts what that label is, the difference between the prediction and the truth gives you a loss, and you learn from that.
But in reinforcement learning, there is no ground-truth action waiting to be revealed. The model chooses an action, observes the consequences, and learns from the reward. To me, that’s a meaningfully different thing from prediction.
I'm not going to stop describing things accurately because someone generated an article that continually undermines its own main point. Limiting the way we talk and think about LLMs to a very narrow set of terms doesn't help us. EDIT: gentler phrasing
> generated an article that continually undermines its own main point. I disagree that this accurately describes TFA.
(I would also agree with the parent commenter on that the writing smells like an LLM.)