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.

翻译：数学推理作为评估人类智能基本认知能力的基石。近年来，面向数学问题自动求解的大型语言模型（LLMs）取得了显著进展。然而，数学问题类型繁多且差异显著，针对LLMs的技术在不同数据集和场景下接受评估，这种多样性使得人们难以清晰辨别这一新兴领域的真实进展与障碍。本综述旨在解决四个关键维度：一，系统探究已研究的各类数学问题及其对应数据集；二，审视面向数学问题求解所提出的各类LLM导向技术；三，概述影响LLMs求解数学问题的因素与关注点；四，阐明该领域持续存在的挑战。据我们所知，本综述是首批对数学领域LLMs全景进行广泛研究的成果之一，为这一快速演进领域的现状、成就与未来挑战提供了全面视角。