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

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

#381

Post by deadbabe »

jerf wrote: post #197

I remember when this stuff was all coming out and people were finally excited about ChatGPT getting the problem with "which is heavier, a 10 pound bag of feathers or a 10 pound bag of bricks?" problem correct. But of course it got it correct. It was in the training set. Vary the problem slightly by just changing the nouns, or changing the numbers so that one in fact was heavier than the other, and performance went al…

If you define a new universe with physical laws that are different from our own, performance will also suffer because there is no training data for that custom universe. While some may say this is cheating, a human would be able to deduce correct answers by understanding the deep implications of the new physical laws in that custom universe.

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

#382

Post by wongarsu »

chongli wrote: post #365
Horffupolde wrote: post #354

Earlier quoted context omitted.

Feathers are less dense so they have higher buoyancy in air, reducing their weight.

Pounds are a unit of weight, not of mass. 10 lbs of feathers is whatever amount of feathers causes a scale to display 10 lbs. If the scale also displays 10 lbs for the quantity of bricks, then they weigh the same, regardless of any differences in mass.

Is this still true? I thought pounds are now defined in terms of kilograms (about 0.453)? Because kilograms are definitely a unit of mass, not weight. Or is the pound defined as some amount of kilograms at a specific point on earth, in a specific phase of the moon?

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

#383

Post by mikeruiz »

whimsicalism wrote: post #342
thaumasiotes wrote: post #318

Earlier quoted context omitted.

>> so far o1-mini has bodied every task people are saying LLMs can’t do in this thread > give me a query and i’ll ask it Here's a query similar to one that I gave to Google Gemini (version unknown), which failed miserably: ---query--- Steeleye Span's version of the old broadsheet ballad "The Victory" begins the final verse with these lines: Here's success unto the Victory / and crew of noble fame and glory to the cap…

i'd prefer an easily verifiable question rather than one where we can always go "no that's not what they really meant" but someone else with o1-mini quota can respond

“They’re toasting Admiral Nelson’s ship (HMS Victory) and its valiant crew, hailing the ship’s successes and Nelson’s heroism. In other words, the singer is offering tribute—“success unto the Victory”—to the vessel and its famed sailors, and “glory to the captain” who led them, namely the celebrated Admiral Horatio Nelson.”

…but to your point, no idea if the artist intended some more obscure reference.

o1-pro was also able to produce a relatively complete version of original source, though, amusingly, referred to it as a ‘broadside’ rather than ‘broadsheet’. Appropriate given the context!

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

#384

Post by chongli »

wongarsu wrote: post #382
chongli wrote: post #365

Earlier quoted context omitted.

Pounds are a unit of weight, not of mass. 10 lbs of feathers is whatever amount of feathers causes a scale to display 10 lbs. If the scale also displays 10 lbs for the quantity of bricks, then they weigh the same, regardless of any differences in mass.

Is this still true? I thought pounds are now defined in terms of kilograms (about 0.453)? Because kilograms are definitely a unit of mass, not weight. Or is the pound defined as some amount of kilograms at a specific point on earth, in a specific phase of the moon?

It seems the pound has since been redefined and split into separate units: pound mass and pound force, the former in terms of kilograms (1 lb = 0.45359237 kg) and the latter in terms of the force exerted by one pound of mass in earth’s gravitational field (standard g = 9.80665m/s^2).

So using the word pound without qualification is ambiguous in contexts where it’s not clear whether mass or force is meant.

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

#385

Post by F7F7F7 »

amluto wrote: post #345
anon946 wrote: post #339

Earlier quoted context omitted.

Add some extra information, and it gets confused. This is 4o. https://chatgpt.com/share/67759723-f008-800e-b0f3-9c81e656d6... One might argue that it's impossible to compress air using known engineering, but that would be a different kind of answer.

It seems more like ChatGPT was asked a rather bizarre question with far too little detail to make sense, and ChatGPT failed to notice or to ask for more information. Although it did get rather impressively confused about the pressure of the air.

“Failed to notice” is a hilariously weird way of talking about tokenization.

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

#386

Post by zahlman »

wongarsu wrote: post #343
mjburgess wrote: post #328

Earlier quoted context omitted.

So do what the commenter suggests and make irrelevant permutations to the input to find when it fails. ie., engage in hypothesis testing rather than confirmation bias. If a system has the capability to solve problems of {parts1...parts_n}, then it only has that capability if irrelevant permutations {parts1..parts2'...parts_n} make no difference. Its very obvious that such permutations can destory such apparent capabi…

If GP's hypothesis was "it fails for small variations of the input, like this one", then testing that hypothesis with that exact variation on a couple models seems fair and scientific. Testing it with more variations until one fails feels a bit like p-hacking. You'd need to engage in actual statistics to get reliable results from that, beyond "If I really try, I can make it fail". Which would be a completely differen…

We aren't testing whether the model's results are stable or correct for a given class of problem. The goal is to establish whether the model can reason.

Nothing capable of reasoning would contradict itself so blatantly and in such a short span while failing to indicate any kind of uncertainty.

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

#387

Post by bcoates »

gus_massa wrote: post #182
whimsicalism wrote: post #143

Earlier quoted context omitted.

but e=mc^2 is just an approximation e: nice, downvoted for knowing special relativity

I didn't downvote it, but short comments are a very big risk. People may misinterpret it, or think it's crackpot theory or a joke and then downvote. When in doubt, add more info, like: But the complete equation is E=sqrt(m^2c^4+p^2) that is reduced to E=mc^2 when the momentum p is 0. More info in https://en.wikipedia.org/wiki/Mass%E2%80%93energy_equivalenc...

This is why RLHF causes those overly verbose answers to simple questions, it's a fundamentally busted evaluation function so you wind up optimizing for the wrong thing

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

#388

Post by stocknoob »

NewsaHackO wrote: post #211
whimsicalism wrote: post #201

Earlier quoted context omitted.

https://chatgpt.com/share/67756897-8974-8010-a0e0-c9e3b3e91f... so far o1-mini has bodied every task people are saying LLMs can’t do in this thread

This happens literally every time. Someone always says "ChatGPT can't do this!", but then when someone actually runs the example, chatGPT gets it right. Now what the OP is going to do next is proceed to move goalposts and say like "but umm I just asked chatgpt this, so clearly they modified the code in realtime to get the answer right"

Similarly, in every thread there’s an AI skeptic who says LLMs are “useless” for coding, and never provides an example query for what they were trying.

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

#389

Post by mjburgess »

jdietrich wrote: post #356
mjburgess wrote: post #328

Earlier quoted context omitted.

So do what the commenter suggests and make irrelevant permutations to the input to find when it fails. ie., engage in hypothesis testing rather than confirmation bias. If a system has the capability to solve problems of {parts1...parts_n}, then it only has that capability if irrelevant permutations {parts1..parts2'...parts_n} make no difference. Its very obvious that such permutations can destory such apparent capabi…

I've just tested a number of permutations with Claude 3.5 Sonnet. It correctly answered all variants I tried on the first attempt, as follows: Which is heavier, a 9.99 kilogram tungsten cube or a 10.01 kilogram block of aerogel? Which is heavier, 10,000 steel balls weighing 0.999 grams each or 10,000 polystyrene balls weighing 1.001 grams each? Which is heavier, a 10.01kg block of steel on Venus or a 9.99kg bag of fe…

Hey, ChatGPT please write me a python program which randomly samples from various materials and various weights then poses a problem to the ChatGPT 4o API -- the goal is to find cases where the LLM fails to obtain the correct answer....

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

#390

Post by dogcomplex »

jerf wrote: post #197

I remember when this stuff was all coming out and people were finally excited about ChatGPT getting the problem with "which is heavier, a 10 pound bag of feathers or a 10 pound bag of bricks?" problem correct. But of course it got it correct. It was in the training set. Vary the problem slightly by just changing the nouns, or changing the numbers so that one in fact was heavier than the other, and performance went al…

lol no, with a small amount of generally-applicable prompt-fu it answers with a ton of nuance that perfectly encapsulates the problem: https://chatgpt.com/share/6775b99d-2220-8003-993b-8dd008a97f...
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