Imagine you have someone polluting your training data every day. That's what happens when you scrape any tech forum today.

The short version is that llm trainign data is the lowest quality data you are likely to see unless you engage in massive potential copyright infringement.

> unless you engage in massive potential copyright infringement. And nobody is going to do that

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.

OpenAI is extremely cagey about what's in their test data set generally, but absent more specific info, they're widely assumed to be grabbing whatever they can. (Notably including copyrighted information used without explicit authorization -- I'll take no position on legal issues in the New York Times's lawsuit against OpenAI, but at the very least, getting their models to regurgitate NYT articles verbatim demonstrates pretty clearly that those articles are in the training set.)

It is on the open internet - questions and suggested solutions:

https://kskedlaya.org/putnam-archive/

I would expect all llms to be trained on it.

Let’s think about this.

> Putnam is not in the test data, at least I haven't seen OpenAI claiming that publicly

What exactly is the source of your belief that the Putnam would not be in the test data? Didn’t they train on everything they could get their hands on?

do you understand the difference between test data and train data? just reread this thread of comments

I don't know why I and you are getting downvoted. Sometimes, HN crowd is just unhinged against AI.

These models are trained in two steps: training base model and then uptraining it. First step includes as much data as possible, everything company can find. For Llama models it's 15T tokens, which is ~40 TB of data. No-one really puts an effort on splitting this data into train/test/eval (and it's not very achievable either). It's just as much data as possible.

So it's like 99.9999999% wrong to assume something public isn't on the train set, such as Putnam problems in this case. This is about it.

right, but where did someone assume it wasn’t in the train set? they just said it wasn’t in the test set

What test set is being talked about here? Why does it matter what’s on this set?

the point is that putnam was never a test/benchmark being used by OpenAI or anyone else, so there is no smoking gun if you find putnam on the train set nor is it cheating or nefarious because nobody ever claimed otherwise.

this whole notion of putnam as test being trained on is a fully invented grievance

read the entire thread in this context

I've read the thread and I think it's not very coherent overall, I also not sure if we disagree =)

I agree that having putnam problems on OpenAI training set is not a smoking gun, however it's (almost) certain they are on training set, and having them would affect performance of the model on them too. Hence research like this is important, since it shows that observed behavior of the models is memoization to large extent, and not necessarily generalization we would like it to be.

nobody serious (like OAI) was using the putnam problems to claim generalization. this is a refutation in search of a claim - and many people in the upstream thread are suggesting that OAI is doing something wrong by training on a benchmark.

OAI uses datasets like frontiermath or arc-agi that are actually held out to evaluate generalization.

I, actually, would disagree with this. To me ability to solve frontiermath does imply ability to solve putnam problems too. Only with putnam problems being easier - they are already been seen by the model, and they are also simpler problems. And just like this - putnam problem with simple changes are also one of the easier stops on the way to truly generalizing math models, with frontiermath being one of the last stops on the way there.

There are benchmarks that are decided beforehand and similar sentences are removed from even the first stage of training. This is useful for tracking model performance and comparing different choices. e.g. see section 'Contamination of downstream tasks' of [1].

Every decent AI lab does this, else the benchmark result couldn't be trusted. OpenAI publishes results of ~20 benchmarks[2] and it is safe to assume they have made reasonable attempt to remove it from training set

[1]: https://arxiv.org/pdf/2107.06499

[2]: https://openai.com/index/hello-gpt-4o/

funny that nobody replying to you seems to even know what a test set is. i always overestimate the depth of ML conversation you can have on HN

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.

putnam isn’t an llm benchmark ahhhh none of these companies are reporting putnam scores there’s nothing nefarious about training on putnam problems

Any problem set that can make news is implicitly an LLM benchmark.

As usual, once a metric becomes a target, it stops being useful.

Well, they are doing BigCorpStuff not Science

I think it's reasonable to assume that openAI is optimising for maximum hype at this point which may include wilfully overfitting for impactful benchmarks to generate positive reports.

When 4 came out they released a document that did BOTH inflate scores by changing the exam conditions, and also bragged about scoring worse than guessing on a multiple choice test.

But putnam isn’t an official test? I find llm discourse on hn so frustrating

How could they remove it?

Those are well known problems, that people talk about on different contexts. They would have to review their entire training set.

I agree that openai is somewhat sketchy about this, but they're sketchy about everything. In the past though they have admitted up front to data contamination (e.g. the original gpt-4 press release did not use big-bench as a benchmark due to data contamination). For the Putnam in particular: this is not a benchmark that they use. There is no reason to exclude it since it is not part of the "test set" in any meaningful sense.