| # MATH | |
| ℹ️ This is the 4-shot variant! | |
| ## Paper | |
| 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: The few-shot and the generated answer extraction is based on the [Minerva](https://arxiv.org/abs/2206.14858) and exact match equivalence is calculated using the `sympy` library. This requires additional dependencies, which can be installed via the `lm-eval[math]` extra. | |
| Homepage: https://github.com/hendrycks/math | |
| ## Citation | |
| ``` | |
| @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} | |
| } | |
| @misc{2206.14858, | |
| Author = {Aitor Lewkowycz and Anders Andreassen and David Dohan and Ethan Dyer and Henryk Michalewski and Vinay Ramasesh and Ambrose Slone and Cem Anil and Imanol Schlag and Theo Gutman-Solo and Yuhuai Wu and Behnam Neyshabur and Guy Gur-Ari and Vedant Misra}, | |
| Title = {Solving Quantitative Reasoning Problems with Language Models}, | |
| Year = {2022}, | |
| Eprint = {arXiv:2206.14858}, | |
| } | |
| ``` | |
| ### Groups and Tasks | |
| #### Groups | |
| - `minerva_math` | |
| #### Tasks | |
| - `minerva_math_algebra` | |
| - `minerva_math_counting_and_prob` | |
| - `minerva_math_geometry` | |
| - `minerva_math_intermediate_algebra` | |
| - `minerva_math_num_theory` | |
| - `minerva_math_prealgebra` | |
| - `minerva_math_precalc` | |
| ### Checklist | |
| The checklist is the following: | |
| For adding novel benchmarks/datasets to the library: | |
| * [x] Is the task an existing benchmark in the literature? | |
| * [x] Have you referenced the original paper that introduced the task? | |
| * [x] 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? | |
| * The implementation in the original paper is one where the model is first fine-tuned on the data. They do have a few-shot evaluation for GPT-3, however the few-shot context used here is sourced from [Lewkowycz et al](https://arxiv.org/abs/2206.14858). The achieved accuracy on Llama-2 models is comparable to that provided in the paper, though not identical. | |
| If other tasks on this dataset are already supported: | |
| * [x] Is the "Main" variant of this task clearly denoted? | |
| * [x] Have you provided a short sentence in a README on what each new variant adds / evaluates? | |
| * [x] Have you noted which, if any, published evaluation setups are matched by this variant? | |
| ### Variant Wishlist | |
| - [ ] zero-shot variant | |