# NaturalProver: Grounded Mathematical Proof Generation with Language Models

@article{Welleck2022NaturalProverGM, title={NaturalProver: Grounded Mathematical Proof Generation with Language Models}, author={Sean Welleck and Jiacheng Liu and Ximing Lu and Hannaneh Hajishirzi and Yejin Choi}, journal={ArXiv}, year={2022}, volume={abs/2205.12910} }

Theorem proving in natural mathematical language – the mixture of symbolic and natural language used by humans – plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained underexplored with modern generative models. We study large-scale language models on two new generation tasks: suggesting the next step in a mathematical proof, and full proof generation. We develop N ATURAL P ROVER , a language model that…

## 4 Citations

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It is shown that small transformers can be trained, from examples only, to compute approximate solutions with more than 90% accuracy (over 99% in most cases), which suggests that applications of transformers to mathematics are not limited to symbolic computation, and can cover a broader range ofScientific problems.

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