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Large Language Models for Mathematicians (arxiv.org)
1 point by belter on Oct 25, 2024 | hide | past | pdf | discuss on HN

In plain words: A survey of how well text-generating AI can help professional mathematicians, including a plain mathematical explanation of the core network design behind such tools. It finds they speed up writing and coding but still make errors in real mathematics, so careful checking is needed.

Abstract

Large language models (LLMs) such as ChatGPT have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code. For many professions, LLMs represent an invaluable tool that can speed up and improve the quality of work. In this note, we discuss to what extent they can aid professional mathematicians. We first provide a mathematical description of the transformer model used in all modern language models. Based on recent studies, we then outline best practices and potential issues and report on the mathematical abilities of language models. Finally, we shed light on the potential of LLMs to change how mathematicians work.

Simon Frieder, Julius Berner, Philipp Petersen, Thomas Lukasiewicz
arXiv:2312.04556 · cs.CL, cs.AI, cs.LG, math.HO · submitted Dec 7, 2023 · updated Apr 2, 2024
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Also discussed: Feb 2025 (89 points, 28 comments) · Dec 2023 (2 points, 0 comments)