AMC_2022_23
Progress Over Time
Interactive timeline showing model performance evolution on AMC_2022_23
AMC_2022_23 Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Mistral AI | 675B | — | — | ||
| 1 | 675B | — | — | |||
| 1 | 675B | — | — | |||
| 1 | Mistral AI | 675B | 262K | $0.50 / $1.50 | ||
| 5 |  Google | — | — | — | ||
| 6 | Google | — | — | — |
What is AMC_2022_23?
American Mathematics Competition problems from the 2022-23 academic year, consisting of multiple-choice mathematics competition problems designed for high school students. These problems require advanced mathematical reasoning, problem-solving strategies, and mathematical knowledge covering topics like algebra, geometry, number theory, and combinatorics. The benchmark is derived from the official AMC competitions sponsored by the Mathematical Association of America.
AMC_2022_23 is a text benchmark evaluating models on math and reasoning tasks. LLM Stats tracks 6 models on this benchmark, scored on a 0–1 scale. The current average is 0.5, with the leader at 0.5.
Compare leaders on the best AI for math and best AI for reasoning leaderboards.
Current leaders
Mistral Large 3 (675B Base) from Mistral AI currently leads the AMC_2022_23 leaderboard with a score of 0.520 across 6 evaluated AI models.
Source paper
- Title
- Measuring Mathematical Problem Solving With the MATH Dataset
- Authors
- Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, and 4 others
- Published
- arXiv
- 2103.03874
Abstract
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. To facilitate future research and increase accuracy on MATH, we also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics. 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.
FAQ
Common questions about the AMC_2022_23 benchmark and leaderboard.