Measuring Mathematical Problem Solving With the MATH Dataset 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.
NOTE: This task corresponds to the MATH (hendrycks_math
) implementation at https://github.com/EleutherAI/lm-evaluation-harness/tree/master . For the variant which uses the custom 4-shot prompt in the Minerva paper (https://arxiv.org/abs/2206.14858), and SymPy answer checking as done by Minerva, see lm_eval/tasks/minerva_math
.
Homepage: https://github.com/hendrycks/math
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
hendrycks_math
: the MATH benchmark from Hendrycks et al. 0- or few-shot.
hendrycks_math_algebra
hendrycks_math_counting_and_prob
hendrycks_math_geometry
hendrycks_math_intermediate_algebra
hendrycks_math_num_theory
hendrycks_math_prealgebra
hendrycks_math_precalc
The checklist is the following:
For adding novel benchmarks/datasets to the library:
- Is the task an existing benchmark in the literature?
- Have you referenced the original paper that introduced the task?
- If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
- Answer extraction code is taken from the original MATH benchmark paper's repository.
If other tasks on this dataset are already supported:
- Is the "Main" variant of this task clearly denoted?
- Have you provided a short sentence in a README on what each new variant adds / evaluates?
- Have you noted which, if any, published evaluation setups are matched by this variant?