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 problems 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 state-of-the-art performance on technical
benchmarks without the use of external tools. 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 third of them.

Minerva 是一个预训练于自然语言数据并进一步训练于技术内容的大型语言模型，通过在技术基准测试中达到最先进的性能，帮助解决了需要量化推理的数学、科学和工程问题的任务，同时在 200 多个需要量化推理的本科水平的物理学、生物学、化学、经济学和其他科学问题上能够正确回答近三分之一的问题。