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> 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 thin

> 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.

gwerbin 1mo ago
But it literally is making a prediction based on the previous X tokens, it's just that X is huge and there is a proportionally huge number of parameters in the token generation function.
- danielmarkbruce 1mo ago
  If you are going to say "literally", then what is your literal definition for the word "prediction" ?
- wat10000 1mo ago
  The discourse around this is annoying. A big group of people use "next-token predictor" to imply that LLMs aren't capable of anything interesting. Another big group of people opposes the use of "next-token predictor" because of that implication. But that fight isn't about the "predictor" language at all. The linked article makes a good point: a substantial chunk of the training does not consist of "here's a bunch of tokens, here's the next token, learn that." But all the comments want to turn it into a referendum on the goodness of AI.
  - deleted 1mo ago
    [deleted]
  - danielmarkbruce 1mo ago
    The fight is about the predictor language in some cases. Because it's only a trivial difference to those who don't understand the details of how these things are made. In pre-training the model really is trained to predict the next token. What is being emitted by the model is, by structure, by training and by optimization, a prediction of the very next token. What is emitted by a model during RLHF and RLVR is not, by structure, training or optimization, a prediction of the next token.
    - wat10000 1mo ago
      Right. But that's never what the discussion is about. This is the first time I've seen someone say, "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token." I have seen many, many people say "it's not just a next-token predictor because there's all this smart stuff going on inside the model." Even here, in the comments for an article that's all about the former, most of the objections to "next-token predictor" are of the latter form.
      - danielmarkbruce 1mo ago
        Yup, you are mostly right. I guess the people in my camp find the "it's just a next token predictor" stupid in that it's like saying "it's just a bunch of carbon and hydrogen", but it's also one of those things where people like to think they are clever because they think they are theoretically correct. But they aren't even that. So it's like double stupid. But the "next token predictor" part is at least technically correct (like, carbon and hydrogen right) for pretraining, so the debate can't really be won there.
        dijksterhuis 1mo ago
        they are cleverly arranged / configured next-most-likely-token predictors, possibly with some clever procedures / attachments on top.
        danielmarkbruce 1mo ago
        Nope. This isn't right.
        dijksterhuis 1mo ago
        > "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token." clever procedures on top of the base transformer architecture. i used simplified words/phrases to summarise the same thing you two were saying (the intent being: here's a version that may be digestible when discussing with others). apparently that means i'm wrong though, no idea why because it seems you've decided to be dismissive rather than constructively elaborate on why this simplified and digestible version might be wrong :shrug:
  - deleted 1mo ago
    [deleted]
  - wonnage 1mo ago
    You can pour a bunch of beads into a pachinko board and end up with a normal distribution. Do the system “know” they need to go there? Then you feed a bunch of tokens into a GPU and end up with a distribution of possible next tokens…
- amluto 1mo ago
  It has nothing to do with X being huge. In fact X might be quite small.

gwerbin 1mo ago

But it literally is making a prediction based on the previous X tokens, it's just that X is huge and there is a proportionally huge number of parameters in the token generation function.

danielmarkbruce 1mo ago
If you are going to say "literally", then what is your literal definition for the word "prediction" ?
wat10000 1mo ago
The discourse around this is annoying. A big group of people use "next-token predictor" to imply that LLMs aren't capable of anything interesting. Another big group of people opposes the use of "next-token predictor" because of that implication. But that fight isn't about the "predictor" language at all. The linked article makes a good point: a substantial chunk of the training does not consist of "here's a bunch of tokens, here's the next token, learn that." But all the comments want to turn it into a referendum on the goodness of AI.
- deleted 1mo ago
  [deleted]
- danielmarkbruce 1mo ago
  The fight is about the predictor language in some cases. Because it's only a trivial difference to those who don't understand the details of how these things are made. In pre-training the model really is trained to predict the next token. What is being emitted by the model is, by structure, by training and by optimization, a prediction of the very next token. What is emitted by a model during RLHF and RLVR is not, by structure, training or optimization, a prediction of the next token.
  - wat10000 1mo ago
    Right. But that's never what the discussion is about. This is the first time I've seen someone say, "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token." I have seen many, many people say "it's not just a next-token predictor because there's all this smart stuff going on inside the model." Even here, in the comments for an article that's all about the former, most of the objections to "next-token predictor" are of the latter form.
    - danielmarkbruce 1mo ago
      Yup, you are mostly right. I guess the people in my camp find the "it's just a next token predictor" stupid in that it's like saying "it's just a bunch of carbon and hydrogen", but it's also one of those things where people like to think they are clever because they think they are theoretically correct. But they aren't even that. So it's like double stupid. But the "next token predictor" part is at least technically correct (like, carbon and hydrogen right) for pretraining, so the debate can't really be won there.
      - dijksterhuis 1mo ago
        they are cleverly arranged / configured next-most-likely-token predictors, possibly with some clever procedures / attachments on top.
        danielmarkbruce 1mo ago
        Nope. This isn't right.
        dijksterhuis 1mo ago
        > "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token." clever procedures on top of the base transformer architecture. i used simplified words/phrases to summarise the same thing you two were saying (the intent being: here's a version that may be digestible when discussing with others). apparently that means i'm wrong though, no idea why because it seems you've decided to be dismissive rather than constructively elaborate on why this simplified and digestible version might be wrong :shrug:
- deleted 1mo ago
  [deleted]
- wonnage 1mo ago
  You can pour a bunch of beads into a pachinko board and end up with a normal distribution. Do the system “know” they need to go there? Then you feed a bunch of tokens into a GPU and end up with a distribution of possible next tokens…
amluto 1mo ago
It has nothing to do with X being huge. In fact X might be quite small.