8 ms·
For some context on why this is important: this benchmark was designed to be extremely challenging for LLMs, with problems requiring several hours or days of wo
by agucova 2y ago
For some context on why this is important: this benchmark was designed to be extremely challenging for LLMs, with problems requiring several hours or days of work by expert mathematicians. Currently, LLMs solve 2% of problems in the set (which is kept private to prevent contamination).
They even provide a quote from Terence Tao, which helped create the benchmark (alongside other Field medalists and IMO question writers):
> “These are extremely challenging. I think that in the near term basically the only way to solve them, short of having a real domain expert in the area, is by a combination of a semi-expert like a graduate student in a related field, maybe paired with some combination of a modern AI and lots of other algebra packages…”
Surprisingly, prediction markets [1] are putting 62% on AI achieving > 85% performance on the benchmark before 2028.
[1]: https://manifold.markets/MatthewBarnett/will-an-ai-achieve-85-performance-o?play=true https://manifold.markets/MatthewBarnett/will-an-ai-achieve-8...
- llm_trw 2y agoThese benchmarks are entirely pointless. The people making them are specialists attempting to apply their skills to areas unrelated to LLM performance, a bit like a sprinter making a training regimen for a fighter jet. What matters is the data structures that underlie the problem space - graph traversal. First, finding a path between two nodes; second, identifying the most efficient path; and third, deriving implicit nodes and edges based on a set of rules. Currently, all LLMs are so limited that they struggle with journeys longer than four edges, even when given a full itinerary of all edges in the graph. Until they can consistently manage a number of steps greater than what is contained in any math proof in the validation data, they aren’t genuinely solving these problems; they’re merely regurgitating memorized information.
- dr_dshiv 2y ago> they’re merely regurgitating memorized information Source?
- llm_trw 2y agoIf a model can't inately reason over 5 steps in a simple task but produces a flawless 500 step proof you either have divine intervention or memorisation.
- NitpickLawyer 2y agoAlphaGeometry has entered the chat. Also, AIMOv2 is doing stage 2 of their math challenge, they are now at "national olympics" level of difficulty. They have a new set of questions. Last year's winner (27/50 points) got 2/50 on the new set. In the first 3 weeks of the competition the top score is 10/50 on the new set, mostly with Qwen2.5-math. Given that this is a purposefully made new set of problems, and according to the organizers "made to be AI hard", I'd say the regurgitation stuff is getting pretty stale. Also also, the fact that claude3.5 can start coding in an invented language w/ ~20-30k tokens of "documentation" about the invented language is also some kind of proof that the stochastic parrots are the dismissers in this case.
- llm_trw 2y agoI've not tested those models. Feel free to flick me through a couple of k in bitcoins if you'd like me to have a look for you.
- exe34 2y agohe just explained it to you.
- firebaze 2y agoI'm not sure if it is feasible to provide all relevant sources to someone who doesn't follow a field. It is quite common knowledge that LLMs in their current form have no ability to recurse directly over a prompt, which inherently limits their reasoning ability.
- dr_dshiv 2y agoI am not looking for all sources. And I do follow the field. I just don’t know the sources that would back the claim they are making. Nor do I understand why limits on recursion means there is no reasoning and only memorization.
- benchmarkist 2y agoIt will be a useful benchmark to validate claims by people like Sam Altman about having achieved AGI.
- mkl 2y agoMost humans can't solve these problems, so it's certainly possible to imagine a legitimate AGI that can't either.
- aurareturn 2y agoBut humans can solve these problems given enough time and domain knowledge. An LLM would never be able to solve them unless they get smarter. Thats the point. It’s not about whether a random human can solve them. It’s whether AI, in general, can. Humans, in general, have proven to be able to solve them already.
- benchmarkist 2y agoThat's correct. Thanks for clarifying for me because I have gotten tired with the comparison to "99% of humans can't do this" as a counter-argument to AI hype criticism.
- llm_trw 2y agoIt is very much an open question just what an llm can solve when allowed to generate an indefinite number of intermediate tokens and allowed to sample an arbitrary amount of text to ground itself. There are currently no tools that let llms do this and no one is building the tools for answering open ended questions.
- mkl 2y agoI'm responding to this: > It will be a useful benchmark to validate claims by people like Sam Altman about having achieved AGI. I think it is possible to achieve AGI without creating an AGI that is an expert mathematician, and that it is possible to create a system that can do FrontierMath without achieving AGI. I.e. I think failure or success at FrontierMath is orthogonal to achieving AGI (though success at it may be a step on the way). Some humans can do it, and some AGIs could do it, but people and AI systems can have human-level intelligence without being able to do it. OTOH I think it would be hard to claim you have ASI if it can't do FrontierMath.
- nopinsight 2y ago> Currently, all LLMs are so limited that they struggle with journeys longer than four edges, even when given a full itinerary of all edges in the graph. This is probably not the case for LLMs in the o1 series and possibly Claude 3.5 Sonnet. Have you tested them on this claim?
- llm_trw 2y agoYes, they also fail. I've found the original gpt4 to be the most consistent. One of these days I'll spend the couple of thousands needed to benchmark all the top models and see how they actually perform on a task which can't be gamed.
- nopinsight 2y agoWhat kinds of problems in what domains did you test o1 models with? I found that they are good at logic and math problems but still hallucinate. I didn’t try to stretch test them with hard problems though.
- llm_trw 2y agoFinding a path between two vertices when given an itinerary of all the edges in a general graph, exactly what I said in the OP.
- youoy 2y agoNot to mention that math proofs are more than graph trasversals... (Although maybe simple math problems are not) There is the problem of extracting the semantics of math formalisms. This is easier in day to day language, I don't know to what extent LLMs can also extract the semantics and relations of different mathematical abstractions.
- sebzim4500 2y ago>Surprisingly, prediction markets [1] are putting 62% on AI achieving > 85% performance on the benchmark before 2028. Or they know the ancient technique of training on the test set. I know most of the questions are kept secret, but they are being regularly sent over the API to every LLM provider.
- tux3 2y agoAlthough the answer isn't sent, so it would have to be a very deliberate effort to fish those out of the API chatter and find the right domain expert with 4-10 hours to spend on cracking it Just letting the AI train on its own wrong output wouldn't help. The benchmark already gives them lots of time for trial and error.
- youoy 2y agoWhy do people still insist that this is unlikely? Like assuming that the company that payed 15M for chat.com does not have some spare change to pay some graduate students/postdocs to solve some math problems. The publicity of solving such benchmark would definitely raise the valuation so it would 100% be worth it for them...
- llm_trw 2y agoAny benchmark which isn't dynamically generated is useless for that very reason.
- rl3 2y agoSimple: I highly doubt they're willing to risk a scandal that would further tarnish their brand. It's still reeling from last year's drama, in addition to a spate of high-profile departures this year. Not to mention a few articles with insider sources that aren't exactly flattering.
- aiono 2y agoI doubt it would be seen as scandal. They can simply generate training data for these questions just like how they generate for other problems. Only difference is probably pay rate is much higher for this kind training data than most other areas.
- light_hue_1 2y agoIf I was going to bet, I would bet yes, they will reach above 85% performance. The problem with all benchmarks, one that we just don't how to solve, is leakage. Systematically, LLMs are much better at benchmarks created before they were trained than after. There are countless papers that show significant leakage between training and test sets for models. This is in part why so many LLMs are so strong according to benchmarks, particularly older popular benchmarks, but then prove to be so weak in practice when you try them out. In addition to leakage, people also over-tune their LLMs to specific datasets. They also go out and collect more data that looks like the dataset they want to perform well on. There's a lot of behind the scenes talk about unethical teams that collect data which doesn't technically overlap test sets, but is extremely close. You can detect this if you look at the pattern of errors these models make. But no one wants to go out and accuse specific teams, at least not for now.
- nerdponx 2y agoCould you run the benchmark by bootstrapping (average of repeated subsampling), instead of a straight-across performance score, and regain some leakage resistance that way? As well as a better simulation of "out of sample" data, at least for a little while.
- agucova 2y agoThis benchmark’s questions and answers will be kept fully private, and the benchmark will only be run by Epoch. Short of the companies fishing out the questions from API logs (which seems quite unlikely), this shouldn’t be a problem.
- benchmarkist 2y agoI looked at the sample questions and even if they get the questions there is no way they will figure out the answers without making significant breakthroughs in understanding mathematics and logic.
- mewpmewp2 2y agoIdeally they would have batches of those exercises, where the only use the next batch when someone has solved a suspicious amount of those exercises. If it performs much worse on the next batch, that is a tell of leakage.
- equestria 2y agoMarket size matters. There's a whopping total of 71 bidders on that.
- TeMPOraL 2y ago> Surprisingly, prediction markets [1] are putting 62% on AI achieving > 85% performance on the benchmark before 2028. Why surprisingly? 2028 is twice as long as capable LLMs existed to date. By "capable" here I mean capable enough to even remotely consider the idea of LLMs solving such tasks in the first place. ChatGPT/GPT-3.5 isn't even 2 years old! 4 years is a lot of time. It's kind of silly to assume LLM capabilities have already bottomed out.
- ekianjo 2y agoSure but it is also reasonable to consider that the pace of progress is not always exponential or even linear at best. Diminishing returns are a thing and we already know that a 405b model is not 5 times better than a 70b model.
- TeMPOraL 2y agoYes, but! Exponential pace of progress isn't usually just one thing; if you zoom in, any particular thing may plateau, but its impact compounds in enabling growth of successors, variations, and related inventions. Nor is it a smooth curve, if you look closely. I feel statements like "a 405b model is not 5 times better than a 70b model" are zooming in on a specific class of models so much you can see the pixels of the pixel grid. There's plenty of open and promising research in tweaking the current architecture in training or inference (see e.g. other thread from yesterday[0]), on top of changes to architecture, methodology, methods of controlling or running inference on exiting models by lobotomizing them or grafting networks to networks, etc. The field is burning hot right now, we're counting space between incremental improvements and interesting research directions in weeks. The overall exponent of "language models" power may just well continue when you zoom out a little bit further. -- [0] - https://news.ycombinator.com/item?id=42093112 https://news.ycombinator.com/item?id=42093112
- mewpmewp2 2y agoHow do you determine the multiplier. Because e.g. there are many problems that GPT4 can solve while GPT3.5 can't. In this case it is infinitely better.
- ak_111 2y agoWould be interesting to know which model solved the 2% and what is the nature of the problems it solved.