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naraburns  nihil supernum  1yr ago (text post)   32490 thread views

Culture War Roundup for the week of February 13, 2023

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No, they don't merely predict the next token

I'm pretty sure this is still how they all work. Predicting the next token is both very hard and very useful to do well in all circumstances!

EDIT: Now that I think about it, I guess with RLHF and other fine-tuning, it'd be fair to say that they aren't "merely" predicting the next token. But I maintain that there's nothing "mere" about that ability.

I mean that with those second-stage training runs (not just RLHF at this point) there no longer exists a real dataset or a sequence of datasets for which the predicted token would be anywhere close to the most likely one. Indeed, OpenAI write

A limitation of this approach is that it introduces an “alignment tax”: aligning the models only on customer tasks can make their performance worse on some other academic NLP tasks.

The «likelihood» distribution is unmoored from its source. Those tokens remain more likely from the model's perspective, but objectively they are also – and perhaps to a greater extent – «truthier», «more helpful» or «less racist» or whatever bag of abstractions the new reward function captures.

This is visible in the increased perplexity, and even in trivial changes like random number lists.

Oh, yes, I totally agree that fine-tuning gives them worse predictive likelihood. I had thought you were implying that the main source of their abilities wasn't next-token prediction, but now I see that you're just saying that they're not only trained that way anymore, which I agree with.

Maybe they meant "they don't merely predict the next token that the user would make".

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