Decoding math : A review of datasets shaping AI-driven mathematical reasoning

Title

Decoding math : A review of datasets shaping AI-driven mathematical reasoning

Creator

Kurisappan, Michael; Sundara Pandiyan, S.

Description

Math problem solving is a fundamental part of the modern era, and artificial intelligence (AI) driven mathematical reasoning has become an essential part of data work. In this literature review, we explore the diverse array of datasets intended to improve AI models capacity to solve mathematical word problems. These datasets not only provided diverse problem sets but also served as benchmarks for evaluating the performance of various deep learning models, including recurrent neural networks (RNNs) and graph-based models. The datasets, particularly GSM8K, posed challenges that even the most sophisticated transformer models struggled to overcome, setting a new standard for the study of AI systems in math problem solving. This literature review aims to provide a comprehensive overview of the evolving landscape of mathematical problem solving, paving the way for future advances in AI-driven mathematical reasoning. 2025, Taru Publications. All rights reserved.

Source

Journal of Interdisciplinary Mathematics;Volume;28;Issue;2;pp.607-625

Date

01-01-2025

Publisher

Taru Publications

Subject

Attention mechanism; Deep learning; Large language models; Mathematical reasoning; Natural language processing; NLP datasets; Question answering; Semantic parsing

Coverage

Kurisappan M., Department of Computer Science and Engineering, Christ University Bangalore, Karnataka, 560074, India; Sundara Pandiyan S., Department of Computer Science and Engineering, Christ University Bangalore, Karnataka, 560074, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 9720502;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.47974/JIM-2105

https://www.scopus.com/pages/publications/105000670318?origin=resultslist