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By requesting access to this dataset, you agree to cite the following work in any publications or projects that utilize this data:
- PutnamBench dataset: @misc{tsoukalas2024putnambenchevaluatingneuraltheoremprovers,
title={PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition},
author={George Tsoukalas and Jasper Lee and John Jennings and Jimmy Xin and Michelle Ding and Michael Jennings and Amitayush Thakur and Swarat Chaudhuri},
year={2024},
eprint={2407.11214},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.11214}
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Note: this is not my work, just uploading for convinience, please cite them.
Dataset Card for PutnamBench
Dataset Description
PutnamBench is a benchmark designed to evaluate theorem-proving algorithms on mathematics problems from the William Lowell Putnam Mathematical Competition (1962–2023). This dataset provides multilingual support for three formal languages: Lean 4, Isabelle, and Coq. It includes 1696 manually crafted formalizations, aggregated across all languages. This HF card is only for the informal stuff hosted at: https://github.com/trishullab/PutnamBench/blob/main/informal/putnam.json
Supported Formal Languages (not in this HF card)
PutnamBench includes formalizations in:
- Lean 4: 644 problems
- Isabelle: 640 problems
- Coq: 412 problems
Statistics
The dataset includes: - Algebra: 253 problems - Analysis: 226 problems - Number Theory: 108 problems - Geometry: 69 problems - Linear Algebra: 51 problems - Abstract Algebra: 28 problems - Combinatorics: 29 problems - Probability: 10 problems - Set Theory: 8 problems
Formal Leaderboard
They are hosting a leaderboard on PutnamBench to share evaluation results. To submit results, please include a link to a preprint or publication. For private submissions, reach out to [email protected].
Citation
If you use this dataset in your research, please cite:
@misc{tsoukalas2024putnambenchevaluatingneuraltheoremprovers,
title={PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition},
author={George Tsoukalas and Jasper Lee and John Jennings and Jimmy Xin and Michelle Ding and Michael Jennings and Amitayush Thakur and Swarat Chaudhuri},
year={2024},
eprint={2407.11214},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.11214},
}
arxiv: https://arxiv.org/abs/2407.11214
Natural Language (Informal) Putnam Benchmark
If you are interested in an natural language automatically evaluatable benchmark, see our Putnam-AXIOM data set and it's functional variations: https://huggingface.co/datasets/Putnam-AXIOM/putnam-axiom-dataset
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