FrontierMath was funded by OpenAI

Can somehow explain to me how they can simply not respect copyright and get away with it? Also is this a uniquely open-ai problem, or also true of the other llm makers?

Why do HN commenters want OpenAI to be considered in violation of copyright here? Ok, so imagine you get your wish. Now all the big tech companies enter into billion dollar contracts with each other along with more traditional companies to get access to training data. So we close off the possibility of open development of AI even further. Every tech company with user-generated content over the last 20 years or so is sitting on a treasure trove now.

I’d prefer we go the other direction where something like archive.org archives all publicly accessible content and the government manages this, keeps it up-to-date, and gives cheap access to all of the data to anyone on request. That’s much more “democratizing” than further locking down training data to big companies.

> This has me curious about ARC-AGI

In the o3 announcement video, the president of ARC Prize said they'd be partnering with OpenAI to develop the next benchmark.

> mechanical turking a training set, fine tuning their model

You don't need mechanical turking here. You can use an LLM to generate a lot more data that's similar to the official training data, and then you can train on that. It sounds like "pulling yourself up by your bootstraps", but isn't. An approach to do this has been published, and it seems to be scaling very well with the amount of such generated training data (They won the 1st paper award)

> OpenAI to have gamed ARC-AGI by seeing the first few examples

not just few examples. o3 was evaluated on "semi-private" test, which was previously already used for evaluating OAI models, so OAI had access to it already for a long time.

In their benchmark, they have a tag "tuned" attached to their o3 result. I guess we need they to inform us of the exact meaning of it to gauge.

Compare:

"O3 performs spectacularly on a very hard dataset that was independently developed and that OpenAI does not have access to."

"O3 performs spectacularly on a very hard dataset that was developed for OpenAI and that only OpenAI has access to."

Or let's put it another way: If what they care about is benchmark integrity, what reason would they have for demanding access to the benchmark dataset and hiding the fact that they finance it? The obvious thing to do if integrity is your goal is to fund it, declare that you will not touch it, and be transparent about it.

If you’re a research lab then yes.

If you’re a for profit company trying to raise funding and fend off skepticism that your models really aren’t that much better than any one else’s, then…

It would be dishonest, but as long as no one found out until after you closed your funding round, there’s plenty of reason you might do this.

It comes down to caring about benchmarks and integrity or caring about piles of money.

Judge for yourself which one they chose.

Perhaps they didn’t train on it.

Who knows?

It’s fair to be skeptical though, under the circumstances.

How would that even work? Aren’t the responses to the API equally fast as the Web interface? Can any human write a response with the speed of an LLM?

> I just saw Sam Altman speak at YCNYC and I was impressed. I have never actually met him or heard him speak before Monday, but one of his stories really stuck out and went something like this:

> "We were trying to get a big client for weeks, and they said no and went with a competitor. The competitor already had a terms sheet from the company were we trying to sign up. It was real serious.

> We were devastated, but we decided to fly down and sit in their lobby until they would meet with us. So they finally let us talk to them after most of the day.

> We then had a few more meetings, and the company wanted to come visit our offices so they could make sure we were a 'real' company. At that time, we were only 5 guys. So we hired a bunch of our college friends to 'work' for us for the day so we could look larger than we actually were. It worked, and we got the contract."

> I think the reason why PG respects Sam so much is he is charismatic, resourceful, and just overall seems like a genuine person.

https://news.ycombinator.com/item?id=3048944

This was my assumption all along.

> What's even more suspicious is that these tweets from Elliot Glazer indicate that they are still "developing" the hold-out set,

There is nothing suspicious about this and the wording seems to be incorrect.

A hold-out set is a percentage of the overall data that is used to test a model. It is just not trained on it. Model developers normally have full access to it.

There is nothing inherently wrong with training on a full/partial hold out set. It just means you have done a different split to train again.

The confusion I see here is that people are equating a hold out set to a blind set. That's a set of data to test against that the model developers (and model) cannot see.

Even so blind sets can also go stale after a few runs and nothing is wrong with ingesting that blind set, as long as you have a new blind set to run against.

Trying to game blind set tests is nothing new and it gets very quickly found out.

What I took from the original article is that the blind set is likely unbalanced and it answered more easier questions than hard ones.

> That’s not really enough to overfit on without causing other problems.

"Causing other problems" is exactly what I'm worried about. I would not put it past OpenAI to deliberately overfit on a set of benchmarks in order to keep up the illusion that they're still progressing at the rate that the hype has come to expect, then keep the very-dangerous model under wraps for a while to avoid having to explain why it doesn't act as smart as they claimed. We still don't have access to this model (because, as with everything since GPT-2, it's "too dangerous"), so we have no way of independently verifying its utility, which means they have a window where they can claim anything they want. If they release a weaker model than claimed it can always be attributed to guardrails put in place after safety testing confirmed it was dangerous.

We'll see when the model actually becomes available, but in the meantime it's reasonable to guess that it's overfitted.

You're missing the part where 25% of the problems were representative of problems top tier undergrads would solve in competitions. Those problems are not based on material that only exists in half a dozen papers.

Tao saw the hardest problems, but there's no concrete evidence that o3 solved any of the hardest problems.

Keep in mind those mathematicians were kept in the dark about the funding: it is incredibly unethical to invite a coauthor to your paper and not tell where the money came from.

It's just incredibly scummy behavior: I imagine some of those mathematicians would have declined the collaboration if the funding were transparent. More so than data contamination, this makes me deeply mistrustful of Epoch AI.

> Because the models have continually matched the quality they claim.

That's very far from true.

"Yes, I know that the HuggingFace arena and coding assistant leaderboards both say that OpenAI's new model is really good, but in practice you should use Claude Sonnet instead" was a meme for good reason, as was "I know the benchmarks show that 4o is just as capable as ChatGPT4 but based on our internal evals it seems much worse". The latter to the extent that they had to use dark UI patterns to hide ChatGPT-4 from their users, because they kept using it, and it cost OpenAI much more than 4o.

OpenAI regularly messes with benchmarks to keep the investor money flowing. Slightly varying the wording of benchmark problems causes a 30% drop in o1 accuracy. That doesn't mean "LLMs don't work" but it does mean that you have to be very sceptical of OpenAI benchmark results when comparing them to other AI labs, and this has been the case for a long time.

The FrontierMath case just shows that they are willing to go much farther with their dishonesty than most people thought.

Thanks for the link. A holdout set which is yet to be used to verify the 25% claim. He also says that he doesn't believe that OpenAI would self-sabotage themselves by tricking the internal benchmarking performance since this will get easily exposed, either by the results from a holdout set or by the public repeating the benchmarks themselves. Seems reasonable to me.

> based on my belief that the brain is nothing special physics-wise and it doesn't manage to realize unknown quantum algorithms in its warm and messy environment

Agreed (well as much as intuition goes), but current gen AI is not a brain, much less a human brain. It shows similarities, in particular emerging multi-modal pattern matching capabilities. There is nothing that says that’s all the neocortex does, in fact the opposite is a known truth in neuroscience. We just don’t know all functions yet - we can’t just ignore the massive Chesterton’s fence we don’t understand.

This isn’t even necessarily because the brain is more sophisticated than anything else, we don’t have models for the weather and immune system or anything chaotic really. Look, folding proteins is still a research problem and that’s at the level of known molecular structure. We greatly overestimate our abilities to model & simulate things. Todays AI is a prime example of our wishful thinking and glossing over ”details”.

> so that classical computers can reproduce all of its feats when having appropriate algorithms and enough computing power.

Sure. That’s a reasonable hypothesis.

> And math reasoning is just another step on a ladder of capabilities, not something that requires completely different approach

You seem to be assuming ”ability” is single axis. It’s like assuming if we get 256 bit registers computers will start making coffee, or that going to the gym will eventually give you wings. There is nothing that suggests this. In fact, if you look at emerging ability in pattern matching that improved enormously, while seeing reasoning on novel problems sitting basically still, that suggests strongly that we are looking at a multi-axis problem domain.

I used to think this, but using o1 quite a bit lately has convinced me otherwise. It’s been 1-shotting the fairly non-trivial coding problems I throw at it and is good about outputting large, complete code blocks. By contrast, Claude immediately starts nagging you about hitting usage limits after a few back and forth and has some kind of hack in place to start abbreviating code when conversations get too long, even when explicitly instructed to do otherwise. I would imagine that Anthropic can produce a good test time compute model as well, but until they have something publicly available, OpenAI has stolen back the lead.

> On a personal level, their model is getting beat handily by Claude Sonnet 3.5 right now. It doesn't seem to show in the benchmarks. I wonder why?

I do use Sonnet 3.5 personally, but this "beat handily" doesn't show on LLM arena. Do OpenAI game that too?

I think “getting beat handily” is a HN bubble concept. Depends on what you’re using it for, but I personally prefer 4o for coding. In enterprise usage, i think 4o is smoking 3.5 sonnet, but that’s just my perception from folks I talk to.

It is known that current models have terrible sample efficiency. I've been told that it's better than I thought it was, but it still isn't good.

I don't think the OP is talking about usefulness at all, that is on a completely different dimension I would say.