Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.
翻译:数学推理是人类智力的一个基本方面,适用于科学、工程、金融和日常生活等各个领域; 能够解决数学问题和证明理论的人工智能系统的发展在机器学习和自然语言处理领域引起了极大的兴趣; 例如,数学是检验各种推理的试金石,这些推理对强大的深层次学习模型、推动新的算法和建模进步具有挑战性; 另一方面,大规模神经语言模型的最近进展为利用深层次学习进行数学推理开辟了新的基准和机会; 在本调查文件中,我们审查了过去十年中数学推理和深层次学习的交叉点的关键任务、数据集和方法; 我们还评估了现有基准和方法,并讨论了该领域今后的研究方向。