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 Conference Paper Faculty Publications- Conference Papers 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 Thakur, Rahul; Poonia, Ramesh Chandra Title A name given to the resource Mathematical Capabilities of LLMs 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 IEEE International Conference on "Computational, Communication and Information Technology", ICCCIT 2025;pp.356-360 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCCIT62592.2025.10928013" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCIT62592.2025.10928013</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002252308?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002252308?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 Thakur R., Christ University, Department of School of Science, Bengaluru, India; Poonia R.C., Christ University, Department of School of Science, Bengaluru, India Description An account of the resource Large language models (LLMs) have the potential to solve mathematical problems well, but little has been investigated. In this research, we evaluate five leading LLMs - (Gemini, Claude, Mistral, ChatGPT, and Llama - on a set of 50 mathematical problems that cover calculus, algebra, geometry, number theory, and probability. Finally, the study evaluates the accuracy of their solutions and gives the ability to assess their intermediate steps correctly. I created a primary dataset for comparison of the LLM performances and ranked the models according to how well they were able to solve those problems. It illustrates the shortcomings of present LLMs at reasoning and solving for mathematics and suggests what needs to be rectified in the LLMs. Future research will further refine this dataset and monitor the progression of LLM capabilities in solving more complex mathematical problems. 2025 IEEE. Subject The topic of the resource dataset evaluation; decision making; LLM benchmarking; mathematical reasoning; model comparison; problem solving Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151296-5; Language A language of the resource English Type The nature or genre of the resource Conference paper 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