Comment by dijksterhuis
2 days ago
prediction is a very specific term of art in the field of machine learning. generally speaking, machine learning models like LLMs are based on probability; performing a statistical prediction of the likely y given some input x
Probability(y | x)
that's why we refer to outputs as a prediction. it is likelihoods and stuff. the output is never definitely correct as we're not dealing with heuristic processes.
> Prediction implies there is some "truth" or event or something that you can test against
there absolutely is a ground truth during training. the core predict-the-next-most-likely-token part of an LLM has a ground truth next-token. that's why you don't end up with generated text like: fish spurious send cattle chocolate phone happy meaning ball orange board canada.
> optimizes to predict the next token in training data
that is the optimization goal in training the next-most-likely-token core of an LLM, it basically translates to maximise the likelihood of predicting the next token x_i given the previous tokens
L(θ) = −log Π^n_{i=1} f_θ(x_i | x1, ..., x_{i−1})
https://arxiv.org/pdf/2012.07805 (GPT2 but the point still stands)
(edit: sorry for the ADHD edits)
Read through the article and comments. You are talking solely about pre-training. I'm talking about post training.
Respectfully, you are miles out of your depth. GPT-2 didn't use any reinforcement learning and is often given as a toy example. That release was 2019 and models now go through a various phases of training with different objective functions and optimizers.
From GP, i.e. the context for this local part of the thread
> Autoregressive LLMs generate tokens one at a time, disputing this is just plain wrong.
next-token prediction i.e. the bit built during pre-training.
at no point in your reply to GP did you specify that you were referring to post-training. respectfully, it seems like this one is on you pal :shrug:
> GPT-2 didn't use any reinforcement learning and is often given as a toy example. That release was 2019 and models now go through a various phases of training with different objective functions and optimizers.
yeah. so? the toy example works for pre-training. see above.
All modern LLMs that actually get used go through post-training. The finished product is something which has been through post training. So they are not next token prediction machines.
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