If 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.

>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.

> 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.

Market size matters. There's a whopping total of 71 bidders on that.

Would be interesting to know which model solved the 2% and what is the nature of the problems it solved.

These 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.

Interesting take sounds like MDL (Minimum description length) for LLMs!

Additional AI math benchmarks:

- "TheoremQA: A Theorem-driven [STEM] Question Answering dataset" (2023) https://github.com/TIGER-AI-Lab/TheoremQA

I wonder if the best benchmark is a Prolog program that generates tests of logical reasoning. You could have a functionally infinite stream of test cases!

I mean, this benchmark is really hard.

I don't think it's a requirement that a system claiming to be AGI should be able to solve these problems, 99.99% of humans can't either.

Hmm. I’m a hard disagree. The problems they show have a number of really nice properties for LLM assessment: They require broad, often integrated knowledge of diverse areas of mathematics, the answers reduce to a number, often a very large number, and thus extremely difficult to guess, and they require a significant amount of symbolic parsing and (I would say) reasoning skills. If we think about what makes a quality mathematician, I’d propose it’s the ability to come at a problem both from the top —- conceptually — and from the bottom — applying various tools and transformations — with a sort of direction in mind that gets to a result.

I’d say these problems strongly encourage that sort of behavior.

I’m also someone who thinks building in abilities like this to LLMs would broadly benefit the LLMs and the world, because I think this stuff generalizes. But, even if not, It would be hard to say that an LLM that could test 80% on this benchmark would be not useful to a research mathematician. Terence Tao’s dream is something like this that can hook up to LEAN, leaving research mathematicians as editors, advisors, and occasionally working on the really hard parts while the rest is automated and provably correct. There’s no doubt in my mind that a high scoring LLM for this benchmark would be helpful in that concept.

I guess the primary reason is that the answers must be numbers that can be verified easily. Otherwise, you just flood the validator with long LLM reasoning that's hard to verify. People have been proposing using LEAN as a medium for answers but AFAIK even LEAN is not mainstream in the general math community, so there's always trade-offs.

Also, coming up with good problems is an art in its own right; the Soviets was famous for institutionalizing anti-Semitism via special math puzzles for Jews in Moscow Univerisity entrance exams. The questions are constructed as such that are hard to solve but have some elementary solutions to divert criticism.