mlsanityData to 7 Oct 2026

Solving Quantitative Reasoning Problems with Language Models

arXiv 2206.14858 ↗First posted 29 Jun 2022cs.CL cs.AI cs.LGcited by 1,359

On the map: LLM reasoning → Mathematical reasoning

Abstract (arXiv)

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.

Key facts. Cited by 1,359 papers in the map (data through 7 Oct 2026). Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. Builds on 32 of its 71 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

32 of 71 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks12 Apr
  2. 2020Generative Language Modeling for Automated Theorem Proving7 Sep

Other references · 30

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  11. 2022Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Proverscited by 97
  12. 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
  13. 2016DeepMath - Deep Sequence Models for Premise Selectioncited by 80
  14. 2021NaturalProofs: Mathematical Theorem Proving in Natural Languagecited by 80
  15. 2018Reinforcement Learning of Theorem Provingcited by 69
  16. 2021LIME: Learning Inductive Bias for Primitives of Mathematical Reasoningcited by 46
  17. 2019EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inferencecited by 39
  18. 2021Fast and Slow Enigmas and Parental Guidancecited by 8

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