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30% drop in O1-preview accuracy when Putnam problems are slightly variated

openreview.net

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Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#41

Post by jsheard »

WiSaGaN wrote: post #35

I don't think this proves that the LLM is just "pattern matcher". Human makes similar mistakes too, especially when under time pressure (similar to non-reasoning model that needs to "use system one" to generate answer on one go). This is further evident that if you specifically ask the models to pay attention to traps, or just ask follow up question "are you sure?", then they usually can get it right.

You're saying that humans perform worse on problems that are slightly different than previously published forms of the same problem? To be clear we are only talking about changing variable names and constants here.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#42

Post by mupuff1234 »

Lerc wrote: post #30
mupuff1234 wrote: post #19

Earlier quoted context omitted.

You're assuming that openAI isn't just gonna add the new questions to the training data.

Their methodology shows they can create an infinite variety of problems. This is the same thing as synthetic training data. It doesn't matter if models are trained on the output of the generated data or not. If the model ends up being able to solve newly generated variations, you'd have to admit that it understands the underlying problems.

I think what it shows that it has minimal "understanding" of the problem - otherwise such small variations wouldn't pose a challenge. Training it to handle these specific small variations doesn't change that.

It's good in automation, not understanding.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#43

Post by Lerc »

dsr_ wrote: post #22
IshKebab wrote: post #20

Earlier quoted context omitted.

> Or it's time to step back and call it what it is - very good pattern recognition. Or maybe it's time to stop wheeling out this tedious and disingenuous dismissal. Saying it is just "pattern recognition" (or a "stochastic parrot") implies behavioural and performance characteristics that have very clearly been greatly exceeded.

Citation needed. Please be more specific, or else this is just a tedious and disingenuous advocacy.

Gpt4 can add very large integers.

It is evident that it is not recalling the sum because all combinations of integer addition were likely not in the training data, Storing the answer to the sum of all integers up to the size that GPT4 can manage would take more parameters than the model has.

That addition is a small capability but you only need a single counterexample to disprove a theory.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#44

Post by exe34 »

jsheard wrote: post #41
WiSaGaN wrote: post #35

I don't think this proves that the LLM is just "pattern matcher". Human makes similar mistakes too, especially when under time pressure (similar to non-reasoning model that needs to "use system one" to generate answer on one go). This is further evident that if you specifically ask the models to pay attention to traps, or just ask follow up question "are you sure?", then they usually can get it right.

You're saying that humans perform worse on problems that are slightly different than previously published forms of the same problem? To be clear we are only talking about changing variable names and constants here.

Often yes, because we assume we already know the answer and jump to the conclusion. At least those of us with ADHD do.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#46

Post by [deleted] »

WiSaGaN wrote: post #35

I don't think this proves that the LLM is just "pattern matcher". Human makes similar mistakes too, especially when under time pressure (similar to non-reasoning model that needs to "use system one" to generate answer on one go). This is further evident that if you specifically ask the models to pay attention to traps, or just ask follow up question "are you sure?", then they usually can get it right.

[deleted]

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#47

Post by Lerc »

jsheard wrote: post #41
WiSaGaN wrote: post #35

I don't think this proves that the LLM is just "pattern matcher". Human makes similar mistakes too, especially when under time pressure (similar to non-reasoning model that needs to "use system one" to generate answer on one go). This is further evident that if you specifically ask the models to pay attention to traps, or just ask follow up question "are you sure?", then they usually can get it right.

You're saying that humans perform worse on problems that are slightly different than previously published forms of the same problem? To be clear we are only talking about changing variable names and constants here.

That is the principle behind the game 'Simon says'

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#48

Post by jprete »

InkCanon wrote: post #10
Trasmatta wrote: post #4

Earlier quoted context omitted.

Not hardcoded, I think it's just likely that those problems exist in its training data in some form

I've always assumed they removed it, because it's such a basic and fundamental part of ML training that you separate your test and train data. And yet I never see any papers even mention if/how they do this. And I wonder if they do, how do they guarantee with high reliability that their massive terabytes of data don't contain the answer.

I don't see any reason to assume they removed it unless they're very explicit about it. Model publishers have an extremely strong vested interest in beating benchmarks and I expect them to teach to the test if they can get away with it.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#49

Post by wim »

One experiment I would love to see, although not really feasible in practice, is to train a model on all digitized data from before the year 1905 (journals, letters, books, broadcasts, lectures, the works), and then ask it for a formula for mass-energy equivalence. A certain answer would definitely settle the debate on whether pattern recognition is a form of intelligence ;)

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#50

Post by [deleted] »

InkCanon wrote: post #10
Trasmatta wrote: post #4

Earlier quoted context omitted.

Not hardcoded, I think it's just likely that those problems exist in its training data in some form

I've always assumed they removed it, because it's such a basic and fundamental part of ML training that you separate your test and train data. And yet I never see any papers even mention if/how they do this. And I wonder if they do, how do they guarantee with high reliability that their massive terabytes of data don't contain the answer.

[deleted]
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