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I asked GPT-NeoX-20B a hundred arithmetic questions

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Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#51

Post by sillysaurusx »

rapiz wrote: post #50
sillysaurusx wrote: post #36

If you want to play with the model, you can (with difficulty) for free at https://goose.ai/playground . You have to log in, but thankfully you can via google. The playground crashes every minute, and the defaults ruin your outputs (temperature 1, really? 0.7 to 0.8 is a necessity, with top-k 40), and they turned off autocorrect on mobile, presumably because they hate you and your family for owning an iPad, but you ca…

Thanks for that. I've played around a little bit. > What is 123456789 - 123456789? > 123456788 > What is 123456789 * 0? > 123456789 Not even near. It didn't surprise me that the model failed to handle cases above, which are unlikely to present in the data set.

Try temp 0.1 top-k 40. For math, it matters to have an unthinkably low temperature. It’s what generated the results in the OP.

What is 12345 - 12345?

0

What is the distance between -0.1 and -0.01?

0.09

One neat example just now:

What is 12345 divided by 12?

4115/4

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#52

Post by machiaweliczny »

rawoke083600 wrote: post #29
d--b wrote: post #24

What? You think this is poor performance? This totally blows my mind. I would never have guessed that GPT could get ANY of these right. I mean, is there a data point in the dataset used to train where you can read 2241 + 19873 = 22114? Quite unlikely... And those multiplications. It's consistently getting the number of digits right and the first two numbers correct. How the hell does this happen? Sure, it's sometimes…

Maybe this is an example of where you need an "extra specialized skill"(arithmetic) vs the general and semi-ambiguous-skill of language+conversation. GPT-3 is "good with conversation (language)" GPT-3 now needs a "sub-nn-model" to do the very 'specialized skill called math' *GPT-3 Should 'learn' to recognize which questions should be delicate to a submodel.

I think this is idea of Google Pathways (Multitude of Expert model). I mean it already works like that in every model but I think they train it differently to have it more separated.

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#54

Post by paraschopra »

I tried putting numbers as words and it did additions perfectly. Pretty magical!

What is fifty plus ninety? 140

What is fifty plus ninety one? 141

What is fifty minus ninety one? -41

What is minus fifty minus ninety one? -141

Although it failed in multiplication or adding longer numbers (as words).

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#55

Post by JonChesterfield »

FeepingCreature wrote: post #49
tsimionescu wrote: post #42

Earlier quoted context omitted.

Sure, but GPT-3 doesn't attach semantics to text, it just learns how to produce text patterns that are similar to text patterns it has seen before.

"Similar" is an inherently semantic property.

Words with the same length are similar to one another but not well correlated semantically. E.g. bog vs dog.

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#56

Post by YeGoblynQueenne »

Isinlor wrote: post #13
williamtrask wrote: post #5

Earlier quoted context omitted.

Poor performance is more likely due to how transformer neural networks view numbers. It memorises them like words instead of modeling their numerical structure. Thus even if it’s seen the number 3456 and 3458, it knows nothing of 3457. Totally different embedding. It’s like a kid memorising a multiplication table instead of learning the more general principle of multiplication (related: this illusion is why big model…

Take a look at this paper: Deep Symbolic Regression for Recurrent Sequences https://arxiv.org/abs/2201.04600 If you look at embedding visualization it is very clear that the model learns order of numbers. (Interactive demo: http://recur-env.eba-rm3fchmn.us-east-2.elasticbeanstalk.com... ) There is also: Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets https://arxiv.org/abs/2201.02177 Again, l…

The "Deep Symbolic Regression" paper reports very poor generalisation results that break off after a small n (where n is the number of tokens in the predicted sequence). It works some of the time for n = 1 (predicts the next token) but accuracy drops off for n = 10. No results are reported for N > 10 as far as I can tell in the "Out of Domain Generalization" section (which is the meat and potatoes of the "generalization" claim).

tl;dr they can sometimes generalise to the next 1 to 10 tokens (digits or operators), but no more.

This kind of short-term "generalisation" on OOD data is standard in neural nets trying to approximate symbolic regressions or things like grammars etc as far as I know.

I do like they use 'Out of Domain" rather than "Out of Distribution" as a target though. That makes more sense.

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#57

Post by NateEag »

wildmanx wrote: post #53

I'd like to see doing this with random people on the street and then compare performance. You may be surprised.

Not really.

Average person on the street is going to correctly say "geez, I dunno. Can I use my phone?"

If you don't forbid them to, then they'll whip it out and get 98% correct (I figure they'll typo a few).

This model didn't have enough understanding to do that (since it literally has no understanding at all).

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#58

Post by [deleted] »

Isinlor wrote: post #18
catach wrote: post #15

Earlier quoted context omitted.

Being able to extrapolate to numbers that were not in the training set, perhaps? At least that'd be a basic part of the requirement.

Sure: Deep Symbolic Regression for Recurrent Sequences https://arxiv.org/abs/2201.04600 (Interactive demo: http://recur-env.eba-rm3fchmn.us-east-2.elasticbeanstalk.com... ) Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets https://arxiv.org/abs/2201.02177 Both of these models can generalize to numbers it have not seen.

[deleted]

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#59

Post by YeGoblynQueenne »

Isinlor wrote: post #18
catach wrote: post #15

Earlier quoted context omitted.

Being able to extrapolate to numbers that were not in the training set, perhaps? At least that'd be a basic part of the requirement.

Sure: Deep Symbolic Regression for Recurrent Sequences https://arxiv.org/abs/2201.04600 (Interactive demo: http://recur-env.eba-rm3fchmn.us-east-2.elasticbeanstalk.com... ) Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets https://arxiv.org/abs/2201.02177 Both of these models can generalize to numbers it have not seen.

As far as I can tell from a quick heuristic perusal, the "Generalization Beyond Overfitting" paper reports "generalisation" _on the validation set_. That's not particularly impressive and it's not particularly "generalisation" either.

Actually, I really don't grokk this (if I may). I often see deep learning work reporting generalisation on the validation set. What's up with that? Why is generalisation on the validation set more interesting than on the test set, let alone OOD data?

Re: I asked GPT-NeoX-20B a hundred arithmetic questions

#60

Post by systemcluster »

sillysaurusx wrote: post #36

If you want to play with the model, you can (with difficulty) for free at https://goose.ai/playground . You have to log in, but thankfully you can via google. The playground crashes every minute, and the defaults ruin your outputs (temperature 1, really? 0.7 to 0.8 is a necessity, with top-k 40), and they turned off autocorrect on mobile, presumably because they hate you and your family for owning an iPad, but you ca…

Thank you for the feedback! We're working on improving the playground and I hope those issues will be a thing of the past soon.
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