学术界的 ChatGPT 和大型语言模型:机遇与挑战。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-07-13 DOI:10.1186/s13040-023-00339-9
Jesse G Meyer, Ryan J Urbanowicz, Patrick C N Martin, Karen O'Connor, Ruowang Li, Pei-Chen Peng, Tiffani J Bright, Nicholas Tatonetti, Kyoung Jae Won, Graciela Gonzalez-Hernandez, Jason H Moore
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引用次数: 0

摘要

2022 年末引入的大型语言模型(LLM)允许迭代式 "聊天",这是一种范式的转变,它能生成与人类所写文本无异的文本。基于 LLM 的聊天机器人在提高学术工作效率方面潜力巨大,但必须考虑其公平使用和固有偏见的伦理影响。在这篇社论中,我们从学者的角度讨论了这项技术在学术写作、教育和编程方面的局限性和实用性。最后,我们对在学术界使用 LLM 和聊天机器人的立场总结如下:(1)我们必须找到有效使用它们的方法;(2)使用它们并不构成剽窃(尽管它们可能会产生剽窃文本);(3)我们必须量化它们的偏见;(4)用户必须警惕它们的低准确性;(5)它们作为学术工具应用于研究的前景是光明的。
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ChatGPT and large language models in academia: opportunities and challenges.

The introduction of large language models (LLMs) that allow iterative "chat" in late 2022 is a paradigm shift that enables generation of text often indistinguishable from that written by humans. LLM-based chatbots have immense potential to improve academic work efficiency, but the ethical implications of their fair use and inherent bias must be considered. In this editorial, we discuss this technology from the academic's perspective with regard to its limitations and utility for academic writing, education, and programming. We end with our stance with regard to using LLMs and chatbots in academia, which is summarized as (1) we must find ways to effectively use them, (2) their use does not constitute plagiarism (although they may produce plagiarized text), (3) we must quantify their bias, (4) users must be cautious of their poor accuracy, and (5) the future is bright for their application to research and as an academic tool.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
期刊最新文献
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