MATH is a large-scale dataset of over 12,000 mathematics problems, designed to test the ability of models to understand and solve mathematical questions. The problems cover a range of topics and difficulty levels, from elementary math to college math, and require various skills, such as arithmetic, algebra, geometry, calculus, logic, and word problems.

MATH is a benchmark for testing the mathematical reasoning ability of large language models (LLMs), such as BERT, GPT-3, and RoBERTa. It can be used to evaluate how well these models can understand natural language, generate mathematical expressions, and solve mathematical challenges. MATH is also intended to encourage the development of more robust and generalizable models that can handle diverse and complex problems.

Here is an example problem from the MATH dataset, taken from the calculus domain:

Find the derivative of f(x) = x^3 + 2x – 1 – A) f’(x) = 3x^2 + 2 – B) f’(x) = 3x^2 – 2 – C) f’(x) = x^2 + 2x – D) f’(x) = x^2 – 2x

The correct answer is A, as the derivative of x^3 is 3x^2, the derivative of 2x is 2, and the derivative of a constant is 0.

• The original paper by Saxton et al. that introduces the dataset and the methodology used to create it.
• The GitHub repository by deepmind that contains the dataset, the code, and the models for MATH.
• The Papers With Code website that lists the papers and code related to the MATH dataset and task.
• The Hugging Face website that provides the dataset download link and the documentation for loading and using the dataset.
• MBPP