AI language models are struggling to “get” math

Post by sdenton4 » Thu, Oct 13, 2022, 1:16 AM UTC

p1esk wrote: post #124

bloep wrote: post #116

Earlier quoted context omitted.

There are quite simple tricks to avoid repetition/copying in NNs, e.g. by (1) training a model to predict the "popularity" of the main model's outputs and penalizing popular/copied productions by backpropping through that model so as to decrease the predicted popularity, or (2) by conditioning on random inputs (LLMs can be prompted with imaginary "ID XXX" prefixes before each example to mitigate repetitions), or (3)…

I'm not sure what you mean by "backpropping through that model so as to decrease the predicted popularity". During training, we train a model to literally reproduce famous chunks of music exactly as they are in the training set. We can also learn to predict popularity at the same time, but we can't backpropagate anything that will reduce popularity, because this would directly contradict the main loss objective of ex…

Post by [deleted] » Thu, Oct 13, 2022, 1:26 AM UTC

mgraczyk wrote: post #91

pessimizer wrote: post #89

Earlier quoted context omitted.

> Current AI models are not "just sort of repeating and copying from memory". This is just an incorrect characterization of how they work and how they perform. You say this, but don't explain how. Because this is exactly what they are doing. > AI skeptics often say things like this I'm not really an AI skeptic. I think that we're very close to AI being indistinguishable from people. There are clearly problems that ne…

I'm disagreeing with the language you are using to characterize models. "copying from memory" implies that there is something being copied, and a memory that you are copying it from. I am pointing out that LLMs do not do this. It's not how they work. If you polled 1M random English speakers randomly and asked them whether or not a system that "just sort of repeating and copying from memory" could produce completely n…

Post by smeagull » Thu, Oct 13, 2022, 1:47 AM UTC

mgraczyk wrote: post #82

pessimizer wrote: post #76

That's because they're not modelling anything. The shocking thing about current AI models is that just sort of repeating and copying from memory what you've heard and seen gets you 97% of the way to imitating a person.* They still need to generate actual models somewhere to create consistency; so many generated images with one eye completely different from the other, or three arms, or fingers that grow into their cel…

This is factually wrong, both in terms of quantity and quality. Current AI models are not "just sort of repeating and copying from memory". This is just an incorrect characterization of how they work and how they perform. AI skeptics often say things like this then backpedal with something like "Well they aren't really repeating what they heard, but their generative model is just a slightly more sophisticated version…

Post by riskneutral » Thu, Oct 13, 2022, 1:57 AM UTC

1024core wrote: post #144

mjburgess wrote: post #27

How much of this is just "AI is bad at everything", but in the math case, it's easier for the lay person to tell . It's all just passable garbled nonesense that the reader (goes to lengths) to interept based on their prior knowledge, which is not expressed in the syntax of what these systems output. In the case of mathematics, we're far less willing to "BS away" the interpretive failures. But if we were equally deman…

See the "joke explaining" examples on Page 36 of the paper: https://arxiv.org/abs/2204.02311 and tell me if it's just "passable garbled nonsense".

Post by qualudeheart » Thu, Oct 13, 2022, 2:11 AM UTC