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by nathan_compton
1130 days ago
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Two things about this. 1. the paper in question demonstrates a formal duality between the transformer architecture and gradient descent. If you take this to indicate that the model reasons in some way, then it would be true of the smallest GPT as well as the largest (it is, after all, a consequence of the architecture rather than anything the model has learned to do per se). In any case, the fact that the model can perform the equivalent of a finite number of gradient-like steps on its way to calculating its final conditioned probabilities doesn't really suggest to me that the model reasons in a general way. 2. You are right that no one disputes the model's ability to memorize (and rephrase). What is at question here is whether the model can reason. If it can do 10 code questions it has seen before but fails to do 10 it hasn't (of similar difficulty) then it strongly suggests that it isn't reasoning its way through the questions, but regurgitating/rephrasing. |
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First of all, coding is one thing where expecting perfect try on first pass makes no sense. That GPT-4 didn't one-shot those problems doesn't mean it can't solve them.
Moreover, all this says if true is that GPT-4 isn't as good at coding as initially thought. Nothing else. Doesn't mean it doesn't reason. There are many other tasks where GPT-4 performs about as well on out of distribution/unseen data