Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kurisappan, Michael; Sundara Pandiyan, S. Title A name given to the resource Decoding math : A review of datasets shaping AI-driven mathematical reasoning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Interdisciplinary Mathematics;Volume;28;Issue;2;pp.607-625 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.47974/JIM-2105" target="_blank" rel="noreferrer noopener">https://doi.org/10.47974/JIM-2105</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000670318?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000670318?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Attention mechanism; Deep learning; Large language models; Mathematical reasoning; Natural language processing; NLP datasets; Question answering; Semantic parsing Publisher An entity responsible for making the resource available Taru Publications Relation A related resource ISSN: 9720502; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online