@inproceedings{cb02085c21b14e07b5eab28ae3f36763,
title = "Capability of LLM-Based AI to Verbalize Math: A User Perspective Case Study",
abstract = "In this paper we present a short study on the user perspective of the capability of LLM-based AI to verbalize mathematical content, i.e. transcribe symbolic notation and formulas into natural spoken language. For the selected base of mathematical expressions, we run a series of experiments and analyze the results of verbalization obtained by prompting LLM in terms of repeatability and precision. As a reference we use the output of the Equation Wizard {\textendash} the efficient rule-based verbalization tool designed and developed by the authors in previous works. Our experiments are performed with the use of ChatGPT 3.5 {\textendash} a popular, free-of charge LLM that is frequently and eagerly used by students. We demonstrate the inconsistency and unrepeatability of LLM output based on repeated verbalization requests, as well as showcase imperfect verbalization.",
keywords = "ChatGPT, LLM, math verbalization, parsing mathematical notation",
author = "Agnieszka Bier and Zdzislaw Sroczynski",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 43rd IBIMA Conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024 ; Conference date: 26-06-2024 Through 27-06-2024",
year = "2025",
doi = "10.1007/978-3-031-79086-7\_3",
language = "English",
isbn = "9783031790850",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "17--29",
editor = "Soliman, \{Khalid S.\}",
booktitle = "Artificial Intelligence and Machine Learning - 43rd IBIMA Conference, IBIMA-AI 2024, Revised Selected Papers",
address = "Germany",
}