Will a machine learning model score above 50.0% on the MATH dataset before 2025?

Resolved

YES1D

1W

1M

ALL

From Hendrycks et al (https://arxiv.org/abs/2103.03874),
> Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers. To measure this ability in machine learning models, we introduce MATH, a new dataset of 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. [...]
> Even though we are able to increase accuracy on MATH, our results show that accuracy remains relatively low, even with enormous Transformer models. Moreover, we find that simply increasing budgets and model parameter counts will be impractical for achieving strong mathematical reasoning if scaling trends continue. While scaling Transformers is automatically solving most other text-based tasks, scaling is not currently solving MATH. To have more traction on mathematical problem solving we will likely need new algorithmic advancements from the broader research community.
In addition,
> It's also worth mentioning the competition maths problems in MATH are designed under the assumption that competitors don't use calculators or script executors. That way, solving them requires making a clever observation or reducing the search space to make the problem tractable. With a script executor, competitors do not need to figure out how to succinctly reason to the conclusion and cleverness is rarely needed.
> There are other competition problems designed to be difficult even with calculators and script executors, but there are not nearly as many of these problems lying around.
The best model in the paper only received an average accuracy of 6.9% on the dataset.
This question resolves to YES if the state-of-the-art average accuracy score on the MATH dataset, as reported prior to January 1st 2025 Eastern Time, is above 50.0%. Credible reports include but are not limited to blog posts, arXiv preprints, and papers. Otherwise, it resolves to NO.
I will use my discretion in determining whether a result should be considered valid. Obvious cheating, such as including the test set in the training data, does not count. Only results that use a no-calculator restriction will count.

# ๐ Top traders

# | Name | Total profit |
---|---|---|

1 | แน84 | |

2 | แน80 | |

3 | แน79 | |

4 | แน67 | |

5 | แน17 |

Sort by:

Seems like this already happened? https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html :O

in terms of development time in the field of AI, 3 years is a lot. I'm confident we'll get there. If this were set to 'by end of 2022' I'd be confident we wouldn't get there.

YES shares

342

267

92

39

35

25

25

A trader sold แน41 NO from 22% to 46%

A trader bought แน13 YES from 4% to 22%

A trader sold แน130 NO from 0.9% to 4%

A trader bought แน300 NO from 77% to 0.9%

A trader sold แน10 NO from 75% to 77%

A trader bought แน10 NO from 77% to 75%

A trader bought แน20 YES from 76% to 77%

A trader bought แน20 YES from 75% to 76%

A trader bought แน50 NO from 87% to 75%

A trader bought แน20 YES from 86% to 87%

A trader bought แน75 YES from 82% to 86%

A trader bought แน200 YES from 55% to 82%

A trader bought แน20 YES from 49% to 55%

A trader bought แน20 YES from 43% to 49%

A trader bought แน20 YES from 35% to 43%

A trader bought แน20 NO from 40% to 35%

A trader bought แน23 NO from 48% to 40%

A trader bought แน20 YES from 38% to 48%

A trader bought แน1 NO from 39% to 38%

A trader bought แน30 NO from 51% to 39%

A trader bought แน2 YES from 50% to 51%