measuring mathematical problem solving with the math dataset

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The MATH Dataset (NeurIPS 2021)

hendrycks/math

Folders and files, repository files navigation, measuring mathematical problem solving with the math dataset.

This is the repository for Measuring Mathematical Problem Solving With the MATH Dataset by Dan Hendrycks , Collin Burns , Saurav Kadavath , Akul Arora , Steven Basart , Eric Tang , Dawn Song , and Jacob Steinhardt .

This repository contains dataset loaders and evaluation code.

Download the MATH dataset here .

Download the AMPS pretraining dataset here .

If you find this useful in your research, please consider citing

Contributors 5

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Measuring Mathematical Problem Solving With the MATH Dataset

Part of Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021) round2

Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt

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.

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Poster in Datasets and Benchmarks: Dataset and Benchmark Poster Session 1

Measuring mathematical problem solving with the math dataset, dan hendrycks · collin burns · saurav kadavath · akul arora · steven basart · eric tang · dawn song · jacob steinhardt.

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.

Datasets: hendrycks / competition_math like 137

The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support (you can use convert_to_parquet from the datasets library). If this is not possible, please open a discussion for direct help.

Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset

Dataset summary.

The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations.

Supported Tasks and Leaderboards

[More Information Needed]

Dataset Structure

Data instances.

A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's \boxed tag.

An example from the dataset is:

Data Fields

• problem : The competition math problem.
• solution : The step-by-step solution.
• level : The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'.
• type : The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.

Data Splits

• train: 7,500 examples
• test: 5,000 examples

Dataset Creation

Curation rationale, source data, initial data collection and normalization, who are the source language producers, annotations, annotation process, who are the annotators, personal and sensitive information, considerations for using the data, social impact of dataset, discussion of biases, other known limitations, additional information, dataset curators, licensing information.

https://github.com/hendrycks/math/blob/main/LICENSE

Citation Information

Contributions.

Thanks to @hacobe for adding this dataset.

Models trained or fine-tuned on hendrycks/competition_math

mosaicml/mpt-7b-8k-instruct

llm-agents/tora-code-34b-v1.0

Llm-agents/tora-code-7b-v1.0, llm-agents/tora-code-13b-v1.0, llm-agents/tora-7b-v1.0, llm-agents/tora-13b-v1.0, space using hendrycks/competition_math 1.

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Measuring Mathematical Problem Solving With the MATH Dataset

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 cur...

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