活动报名｜语言模型如何学习数学？数学语言模型综述

报告主题：语言模型如何学习数学？数学语言模型综述

报告日期：1月19日(周五) 10:30-11:30

主题简介：

近年来，语言模型（LM），包括预训练语言模型（PLM）和大型语言模型（LLM），在数学领域取得了显著进展。本文对数学语言模型进行了全面的调研，从任务和方法论两个不同的角度系统地分类了关键的研究工作。针对现有大量的数学LLM，进一步细分为指令学习、基于工具的方法、基础思维链推理（CoT）技术和高级CoT方法。

此外，我们还调研了60多个数学数据集，包括训练数据集、基准数据集和增强数据集。最后，本文探讨了数学LM领域内的主要挑战，并给出了该领域的未来发展方向。

In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics. This paper conducts a comprehensive survey of mathematical LMs, systematically categorizing pivotal research endeavors from two distinct perspectives: tasks and methodologies.  The landscape reveals a large number of proposed mathematical LLMs, which are further delineated into instruction learning, tool-based methods, fundamental CoT techniques, and advanced CoT methodologies. In addition, our survey entails the compilation of over 60 mathematical datasets, including training datasets, benchmark datasets, and augmented datasets. Addressing the primary challenges and delineating future trajectories within the field of mathematical LMs, this survey is positioned as a valuable resource, poised to facilitate and inspire future innovation among researchers invested in advancing this domain.

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