The other reason this seems hard to guess is because we don't know how much of what we are asking is in the training data. It would perform on some tasks, while fail at others even though those are similar.
30% drop in O1-preview accuracy when Putnam problems are slightly variated
31–40 of 558 posts
Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#32Earlier 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.
Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#33Earlier quoted context omitted.
Basically yet another proof that we have managed to perfectly recreate human stupidity :-)
Good students are immune to variations that are discussed in the paper. But most academic tests may not differentiate between them and the crammers.
I don't believe that. I'd put some good money that if an excellent student is given an exact question from a previous year, they'll do better (faster & more accurate) on it, than when they're given a variation of it.
Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#34Earlier quoted context omitted.
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.
First of all, Putnam is not in the test data, at least I haven't seen OpenAI claiming that publicly. Secondly, removing it from internet data is not 100% accurate. There are translations of the problems and solutions or references and direct match is not enough. MMLU and test set benchmarks show more resilience though in some previous research.
Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#35Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#36I hope someone reruns this on o1 and eventually o3. If o1-preview was the start like gpt1, then we should expect generalization to increase quickly.
I don't think llm generalise much, that's why they're not creative and can't solve novel problems. It's pattern matching with a huge amount of data. Study on the topic: https://arxiv.org/html/2406.15992v1 This would explain o1 poor performance with problems with variations. o3 seems to be expensive brute forcing in latent space followed by verification which should yield better results - but I don't think we can call…
Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#37Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#38This result is the same as a recent test of the same method+hypothesis from a group at Apple, no? I don’t have that reference handy but I don’t think I’m making it up.
I think you are probably referring to the following paper: https://arxiv.org/abs/2410.05229
I am impressed by the progress on LLMs but I remain skeptical that they can replace humans.
Perhaps some (distant!) future model but I don’t fear mass unemployment (for example) or even moderate LLM-driven unemployment in the near-to-medium term.
They can clearly complement human labor but there are vanishingly few domains where they can be substitutes.
Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated
#39Using LLMs for anything serious and which requires consistency and trustworthiness without hallucinations is irresponsible and ridiculous.
Closed source LLMs are a bubble and a joke.