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arXiv 2312.04556cs.CLcs.AIcs.LGmath.HO
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面向数学家的大型语言模型

Large Language Models for Mathematicians

University of Oxford(牛津大学);University of Vienna(维也纳大学);Vienna University of Technology(维也纳技术大学)

Simon Frieder, Julius Berner, Philipp Petersen, Thomas Lukasiewicz

AI总结:

本文探讨了大型语言模型辅助数学家工作的潜力,通过给出transformer模型的数学描述、总结最佳实践与问题并评估其数学能力,展望了其改变数学家工作方式的前景。

摘要:

诸如ChatGPT之类的大型语言模型(LLMs)因其通用语言理解能力,尤其是生成高质量文本或计算机代码的能力,而受到了极大的关注。对于许多职业而言,LLMs是一种宝贵的工具,可以加快工作速度并提高工作质量。在这篇札记中,我们讨论它们在多大程度上能够帮助专业数学家。我们首先给出所有现代语言模型中使用的transformer模型的数学描述。基于近期研究,我们随后概述最佳实践和潜在问题,并报告语言模型的数学能力。最后,我们阐明LLMs改变数学家工作方式的潜力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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