Mathematical reasoning serves as a cornerstone for assessing the fundamental
cognitive capabilities of human intelligence. In recent times, there has been a
notable surge in the development of Large Language Models (LLMs) geared towards
the automated resolution of mathematical problems. However, the landscape of
mathematical problem types is vast and varied, with LLM-oriented techniques
undergoing evaluation across diverse datasets and settings. This diversity
makes it challenging to discern the true advancements and obstacles within this
burgeoning field. This survey endeavors to address four pivotal dimensions: i)
a comprehensive exploration of the various mathematical problems and their
corresponding datasets that have been investigated; ii) an examination of the
spectrum of LLM-oriented techniques that have been proposed for mathematical
problem-solving; iii) an overview of factors and concerns affecting LLMs in
solving math; and iv) an elucidation of the persisting challenges within this
domain. To the best of our knowledge, this survey stands as one of the first
extensive examinations of the landscape of LLMs in the realm of mathematics,
providing a holistic perspective on the current state, accomplishments, and
future challenges in this rapidly evolving field.

数学推理是评估人类智能基本认知能力的基石。该研究调查了大型语言模型在解决数学问题方面的真正进展、障碍、数学问题类型和相关数据集、解决数学问题的 LLM 技术范围、影响 LLMs 解决数学问题的因素和问题，并提供了这一快速发展领域中的现状、成就和未来挑战的整体观点。