Solving Quantitative Reasoning Problems with Language Models
Abstract
Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally struggled with tasks that require quantitative reasoning, such as solving mathematics, science, and engineering questions at the college level. To help close this gap, we introduce Minerva, a large language model pretrained on general natural language data and further trained on technical content. The model achieves strong performance in a variety of evaluations, including state-of-the-art performance on the MATH dataset. We also evaluate our model on over two hundred undergraduate-level problems in physics, biology, chemistry, economics, and other sciences that require quantitative reasoning, and find that the model can correctly answer nearly a quarter of them.
Cite
Text
Lewkowycz et al. "Solving Quantitative Reasoning Problems with Language Models." Neural Information Processing Systems, 2022.Markdown
[Lewkowycz et al. "Solving Quantitative Reasoning Problems with Language Models." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/lewkowycz2022neurips-solving/)BibTeX
@inproceedings{lewkowycz2022neurips-solving,
title = {{Solving Quantitative Reasoning Problems with Language Models}},
author = {Lewkowycz, Aitor and Andreassen, Anders and Dohan, David and Dyer, Ethan and Michalewski, Henryk and Ramasesh, Vinay and Slone, Ambrose and Anil, Cem and Schlag, Imanol and Gutman-Solo, Theo and Wu, Yuhuai and Neyshabur, Behnam and Gur-Ari, Guy and Misra, Vedant},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/lewkowycz2022neurips-solving/}
}