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Yeah, it sounds like this is just a disagreement about what the word "next" means. I agree with you that "next" just means "the one about to come", and if the u
by saghm 1mo ago
Yeah, it sounds like this is just a disagreement about what the word "next" means. I agree with you that "next" just means "the one about to come", and if the underlying model works by using some prediction mechanism to determine that, then it's by definition a next-token predictor. Disagreeing with that on the basis that the "next" token isn't necessarily in the training data verbatim just seems like an overly strict definition of the word "next".
- jameshart 1mo agoI think it’s a disagreement about what ‘predict’ means. The OP is arguing against people who think that an LLM is ‘predicting’ what token would likely follow if the text preceding were found among the corpus it was originally trained on. Instead it is ‘predicting’ what token would follow if the text were found among really good examples of the text it has being reinforced to produce - be that ‘chats with a helpful assistant’ or ‘sets of changes to a codebase’. And that isn’t really ‘prediction’, so much as ‘generation’. It’s not been tuned to ‘guess the next token right’. It’s been tuned to generate the token that leads to it ultimately scoring highest on its reward function. It’s not predicting the token, it’s predicting the reward.
- danielmarkbruce 1mo agoyes, it's exactly this. And it's not a trivial distinction.
- grebc 1mo agoIt’s deterministically trivial.
- danielmarkbruce 1mo agoPost train a model, you'll be able to determine it is not.
- grebc 1mo agoThe 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.
- danielmarkbruce 1mo agoNope, 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.
- grebc 1mo agoYes, no logic is necessary for LLM adherents we're all finding out. Carry on good soldier.
- danielmarkbruce 1mo agoIf you haven't built one, and don't understand how they work, why comment?
- grebc 1mo agoYou don't need to build a car to understand one. That you tie yourself up in knots of fancy acronyms instead of plain words and that your argument boils down to semantics of the word prediction, it's pretty clear what is up brother.
- danielmarkbruce 1mo agoLol, sure, just read a blog post and you'll understand how a car works....It's very simple....
- 1mo ago
- saghm 1mo agoThat 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.
- jameshart 1mo agoBut 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’.
- saghm 1mo agoI don't find the fact that I don't call any humans "action predictors" to be a particularly meaningful insight because my rationale is that it's a weird thing to call a human; football coaches can do plenty of other things besides just coaching football.
- jameshart 1mo agoYep, we’re all just putting one foot in front of the other, hoping we’re doing the right thing to bring about the outcomes we want, trying our best. But that’s the point: so is an LLM. Putting one token in front of another, hoping it’s doing the right thing to bring about the rewards it’s trained to… trying its best. So yeah, not ‘next token predictors’. ‘Next token tryers’ maybe.
- saghm 1mo agoThere's a pretty huge difference in our understanding of the methodology of how LLMs make decisions and how humans make decisions, so I don't understand why you're arguing that anything about how humans make decisions is relevant to the terminology we use for LLMs.
- 1mo ago
- search_facility 1mo agoWell said!
- grey-area 1mo agoThis is correct for areas where they have been intensively trained to be right, but the training covers a tiny slice of the space of text the LLM must produce and is just adjusting the weights a little. The corpus does still weigh heavily. That’s how they can reliably produce grammatically correct text. That’s also why they sometimes produce nonsense even in domains they are trained on, and more often where there was no training. For example ask it for a recipe for rock pizza or glue pizza or whatever and if it had not been specifically trained on it or had guardrails introduced, but has some nonsense in its dataset, it will reproduce the nonsense.
- leobg 1mo agoOr about what the word “is” means. If you’re Bill Clinton. :-D
- KETHERCORTEX 1mo ago> that isn’t really ‘prediction’, so much as ‘generation’ It's both. Generation in a predictive way. That's what G and P in GPT stand for.
- bjourne 1mo agoThe argument is that a modern LLM cares little for the MLE. Hence, statistically speaking, they are not predictors.