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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

#271

Post by mmmore »

jsheard wrote: post #105
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

It certainly feels like certain patterns are hardcoded special cases, particularly to do with math. "Solve (1503+5171)*(9494-4823)" reliably gets the correct answer from ChatGPT "Write a poem about the solution to (1503+5171)*(9494-4823)" hallucinates an incorrect answer though That suggests to me that they've papered over the models inability to do basic math, but it's a hack that doesn't generalize beyond the simpl…

There's a few things there that could be going on that seem more likely than "hardcoded".

1. The part of the network that does complex math and the part that write poetry are overlapping in strange ways.

2. Most of the models nowadays are assumed to be some mixture of experts. So it's possible that saying write the answer as a poem activates a different part of the model.

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

#272

Post by mmmore »

ankit219 wrote: post #173
e1g wrote: post #63

Earlier quoted context omitted.

I recently watched two interviews with OpenAI researchers where they describe that the breakthrough of o-series (unlike GPT series) is to focus on test time compute as they are designed to “think” more specifically to avoid pattern matching. Noam Brown https://youtu.be/OoL8K_AFqkw?si=ocIS0YDXLvaX9Xb6&t=195 and Mark Chen https://youtu.be/kO192K7_FaQ?si=moWiwYChj65osLGy

Thank you, this is helpful. The post on X was seemingly wrong.

The comment was likely that there's no explicit search. In o1, the model has learned how to search using its context. Presumably they do this by RLing over long reasoning strings/internal monologues.

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

#273

Post by jsheard »

jstummbillig wrote: post #259
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…

FYI: If you do that without a subscrpition, you currently (most likely) get a response generated through 4o-mini — which is not any of their reasoning models (o1, o1-mini or previously o1-preview) of the branch discussed in the linked paper. Notably, it's not even necessarily 4o, their premiere "non-reasoning"-model, but likely the cheaper variant: With a free account the model it claims to be using is "4o auto", whi…

There doesn't seem to be a way to choose a model up-front with a free account, but after you make a query you can click on the "regenerate" button and select whether to try again with "auto", 4o, or 4o-mini. At least until you use 4o too many times and get rate limited.

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

#274

Post by whimsicalism »

chongli wrote: post #269
mortehu wrote: post #213

Earlier quoted context omitted.

While this may be true, it's a very common problem that people who want to demonstrate how bad a model is fail to provide a direct link or simply state the name of the model.

OpenAI is not doing us any favours by using confusing naming schemes for their models and obscuring which models people are actually working with. If I didn’t know any better, I’d say OpenAI doesn’t want us doing these tests accurately and is trying to hide something.

it’s extremely easy to see which model you are using. one’s own… difficulties understanding are not a conspiracy by OpenAI

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

#275

Post by [deleted] »

whimsicalism wrote: post #247
amluto wrote: post #237

Earlier quoted context omitted.

This is all sort of silly IMO. The equation, like basically all equations, needs context. What’s E? What’s m? If E is the total energy of the system and m is the mass (inertial or gravitational? how far past 1905 do you want to go?), then there isn’t a correction. If m is rest mass and E is total energy, then I would call it flat-out wrong, not merely approximate. After all, a decent theory really ought to reproduce…

inertial mass, rest mass, gravitational mass - these are essentially all the same thing. “relativistic mass” is an additional concept where we rewrite energy as mass and is considered archaic

[deleted]

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

#276

Post by chongli »

whimsicalism wrote: post #274
chongli wrote: post #269

Earlier quoted context omitted.

OpenAI is not doing us any favours by using confusing naming schemes for their models and obscuring which models people are actually working with. If I didn’t know any better, I’d say OpenAI doesn’t want us doing these tests accurately and is trying to hide something.

it’s extremely easy to see which model you are using. one’s own… difficulties understanding are not a conspiracy by OpenAI

It does not show the model version anywhere on the page on chatgpt.com, even when logged in.

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

#277

Post by ImPostingOnHN »

greentxt wrote: post #238
ImPostingOnHN wrote: post #224

Earlier quoted context omitted.

Since we know an LLM does indeed simply regurgitate data, having it pass a "test for intelligence" simply means that either the test didn't actually test intelligence, or that intelligence can be defined as simply regurgitating data.

Intelligence is debateble without even bringing ai into it. Nobody agrees on whether humans have intelligence. Well, smart people agree but those people also agree we have or will soon have agi or something negligibly different from it.

> Intelligence is debateble without even bringing ai into it. Nobody agrees on whether humans have intelligence.

Yep, that constitutes the second of the two options I mentioned.

> Well, smart people agree but those people also agree we have or will soon have agi or something negligibly different from it.

lol, the ol' "I know what all smart people think and it's what I think" appeal.

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

#278

Post by neom »

umeshunni wrote: post #196
neom wrote: post #163

Earlier quoted context omitted.

https://s.h4x.club/bLuNed45 - it's more crazy to me that my wife CAN in fact read this stuff easily, vs the fact that an LLM can't. (for anyone who doesn't feel like downloading the zip, here is a single image from the zip: https://s.h4x.club/nOu485qx )

Super interesting in that 1. In theory these kind of connections should be something that LLMs are great at doing. 2. It appears that LLMs are not trained (yet?) on cursive and other non-print text

Yes, I regularly encourage my wife to approach the comp sci department at her uni on doing a project together but she for whatever reason doesn't think they would be interested/I've yet to get her interested enough to grasp what a transformer can do. I find it very frustrating because of your first point, she very specifically could do some meaningful % more research if the LLMs could help with the connections. Sadly, I am not rich, handsome or talented enough to do this for her.

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

#279

Post by fragmede »

cogman10 wrote: post #71
coder543 wrote: post #53

Earlier quoted context omitted.

One problem from the benchmark: "prompt_id": "river_crossing_easy", "category": "Logic Puzzle", "title": "Easy river crossing", "prompt": "A farmer is on one side of a river with a wolf, a goat, and a cabbage. When he is crossing the river in a boat, he can only take one item with him at a time. The wolf will eat the goat if left alone together, and the goat will eat the cabbage if left alone together. How can the fa…

No. Simply plug in the prompt to chat gpt and see what happens. The llm isn't getting confused by the meaning of "item". It's recognizing a common problem and not picking up on the fact that the farmer just needs to transport the goat and nothing else. Instead, it gives the standard answer for how to transport everything across.

I'll admit as a fallible humane I didn't pick it up, but I was focused on the wrong thing because I've been using "and the boat can take everything" and gpt-3 just could not get that variation in one shot.

Gpt-3 is old hat though. later versions of gpt-4 manage to get it with a bunch coaching, and o1 manages to solve it with less coaching.

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

#280

Post by MattDaEskimo »

whimsicalism wrote: post #230
MattDaEskimo wrote: post #227

Earlier quoted context omitted.

Sure, it did good in frontiermath. That's not what this thread is about. Your comment isn't relevant at all

this thread is about math LLM capability, it’s a bit ridiculous to say that mentioning frontiermath is off topic but that’s just me

Just because you can generalize the topic doesn't mean you can ignore the specific conversation and choose your hill to argue.

Additionally, the conversation of this topic is about the model's ability to generalize and it's potential overfitting, which is arguably more important than parroting mathematics.

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