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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.
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
What’s more interesting to me is its application at training time. In reinforcement learning, there is no ground-truth next token to predict.
So if you’re comfortable calling Deep Blue a “next move predictor,” then I think it’s perfectly consistent to call an LLM a “next token predictor.” But I think it’s more useful to think of Deep Blue as evaluating the value of possible moves. roughly, how likely they are to lead to winning.
And I think effectively the same distinction applies here.
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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.
It was written by a human. There are AI edits but it’s very much a human composition. Perhaps a bit sloppy.
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Blog articles from Anthropic and others show that this is not true. A LLM already knows more tokens than the current one. It was mentioned in a blog post about how a LLM is doing haikus and co. There are also structures in an LLM which allows it to 'estimate' numbers to a certain degree and doing other things.
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…
If we suppose that the word "know" can sanely be applied to LLMs at all, then "A LLM already knows more tokens than the current one." seems to me like a perfectly reasonable restatement of that, and not any kind of misinterpretation.
> There's no revision or means of correction, and sometimes this can lead an LLM to route around its own earlier mistakes or simply produce false statements/hallucinations instead of going back and fixing them.
Yes. There is no contradiction. Similarly, when humans speak, we surely have in mind the next few words we're going to say (or at least partial information about them), and may not realize the fault in them until after hearing ourselves utter them. But LLMs are not trained to output "excuse me, I mean…" sorts of things, because they're expected to output primarily as text (which might possibly then be fed to TTS).
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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.
> I'm not sure what you want me to do with that information For example, you could cite specific things that you believe to be "AI tells" or "admissions".
> Strictly speaking, the statement “LLMs are next-token predictors” isn’t wrong, but it’s incomplete.
The article is about how 'next-token predictor' is the wrong mental model; it opens with the admission that it is not the wrong mental model.
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Blog articles from Anthropic and others show that this is not true. A LLM already knows more tokens than the current one. It was mentioned in a blog post about how a LLM is doing haikus and co. There are also structures in an LLM which allows it to 'estimate' numbers to a certain degree and doing other things.
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…
It’s written in Claudish, or perhaps a human who has been reading too much Claudish recently. I am starting to become allergic to Claudish. Not there fully yet — but it’s at a tipping point genuinely worth exploring and worth being precise about.
To be fair, is there anyone who hasn't been "reading too much Claudish recently" who is also qualified to write on the topic?
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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.
> I'm not sure what you want me to do with that information For example, you could cite specific things that you believe to be "AI tells" or "admissions".
To summarize: yes, RLVR and other synthetic training methods exist! It’s still a next-token predictor, and it does not “learn” or “think” or “reason” in the human sense, like so many people seem to believe.
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That seems overly pedantic to me. If I asked you "What's your prediction for the Super Bowl?", I'm pretty confident you would infer that I mean predicting the outcome , not the event itself.
But if you are an NFL coach and I ask you to decide your next action in order to maximize your odds of winning the superbowl , while yes that does involve you having some predictive ability to think about what impact your actions would have on your odds of winning the Super Bowl… I don’t think you would call the process that you use to decide that next action ‘prediction’.
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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.
Nope, it doesn't. 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.
Carry on good soldier.