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Overview: This book constitutes the refereed proceedings of the 17th International Conference on Intelligent Computer Mathematics, CICM 2024, held in Montréal, Québec, Canada, during August 5–9, 2024. The 21 full papers presented were carefully reviewed and selected from 28 submissions. These papers have been categorized into the following sections: AI and LLM; Proof Assistants; Logical Frameworks and Transformations; Knowledge Representation and Certification; Proof Search and Formalization & System Descriptions. The Conference on Intelligent Computer Mathematics (CICM) brings together the many separate communities that have developed theoretical and practical solutions for mathematical applications in Artificial Intelligence, computation, deduction, knowledge management, or user interfaces. This paper explores the potential of leveraging Large Language Models (LLMs) for the tasks of automated annotation and Part-of-Math (POM) tagging of equations. Traditional methods for math term annotation and POM tagging rely heavily on manually crafted rules and limited datasets, which often result in scalability issues and insufficient adaptability to new domains. In contrast, LLMs, with their vast knowledge and advanced natural language understanding capabilities, present a promising alternative. Our methodology involves crafting prompts for LLMs to elicit answers that can be read as key-value pairs where the keys are math terms and the values are the corresponding annotations.
Genre: Non-Fiction > Tech & Devices
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