Comment by Planktonne

2 days ago

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

22 comments

Planktonne

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garrinm  2 days ago

The distinction I perhaps didn’t make clearly enough is that I’m not really debating the concept of prediction at inference time, although, as I pointed out elsewhere, I think that’s the less interesting interpretation of what “prediction” means.

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.

  • Planktonne  2 days ago

    I think you're trying to limit the meaning of both 'next' and 'prediction' in ways that don't reflect usage and that--if adopted--would severely limit our ability to discuss and evolve what LLMs are actually doing.

    There's nothing inherent in either word that forces such a limit; predicting based on what will lead to success as measured by [reward function] is still a prediction.

    • danielmarkbruce  2 days ago

      It's not a prediction of the next move though, and that is the point. It's a prediction of what will happen if you make that move.

      So, it's not a next move predictor. It's a game result predictor.

      4 replies →

zahlman  2 days ago

> generated an article that continually undermines its own main point.

I disagree that this accurately describes TFA.

  • deathanatos  2 days ago

    The entire second on chess engines is, from the view of the entire thesis of TFA, is incoherent. Let's assume, for sake of argument, that I agree with the section: that an idealized chess move predictor isn't a predictor — which is not a thing that exists, as the space of chess is enormous, but let's pretend! — that's not what LLMs are? Even if we just restrict ourselves to the space of written English prose, the space is quite literally infinite. So, hopefully obviously, no LLM is comparable to an idealized chess engine. Similarly, incoherently, we wave away the "make_more_likely", when, at least to me, the entire meat of that argument would be in the reward function, and we just gloss over that entirely.

    (I would also agree with the parent commenter on that the writing smells like an LLM.)

    • astrobe_  2 days ago

      The reward function seems indeed to be the protagonist there, still it stays in the shadows. One can only imagine that it is some kind of evaluator that scores the sequence based on grammar correctness, semantic consistency, etc.? To use the proposed chess analogy, maybe it could be a Stockfish engine that evaluates the submitted position that results from the move submitted by the LLM?

  • Planktonne  2 days ago

    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.