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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 implicatio...

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Autores principales: Meyer, Jesse G., Urbanowicz, Ryan J., Martin, Patrick C. N., O’Connor, Karen, Li, Ruowang, Peng, Pei-Chen, Bright, Tiffani J., Tatonetti, Nicholas, Won, Kyoung Jae, Gonzalez-Hernandez, Graciela, Moore, Jason H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339472/
https://www.ncbi.nlm.nih.gov/pubmed/37443040
http://dx.doi.org/10.1186/s13040-023-00339-9
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author Meyer, Jesse G.
Urbanowicz, Ryan J.
Martin, Patrick C. N.
O’Connor, Karen
Li, Ruowang
Peng, Pei-Chen
Bright, Tiffani J.
Tatonetti, Nicholas
Won, Kyoung Jae
Gonzalez-Hernandez, Graciela
Moore, Jason H.
author_facet Meyer, Jesse G.
Urbanowicz, Ryan J.
Martin, Patrick C. N.
O’Connor, Karen
Li, Ruowang
Peng, Pei-Chen
Bright, Tiffani J.
Tatonetti, Nicholas
Won, Kyoung Jae
Gonzalez-Hernandez, Graciela
Moore, Jason H.
author_sort Meyer, Jesse G.
collection PubMed
description 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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spelling pubmed-103394722023-07-14 ChatGPT and large language models in academia: opportunities and challenges Meyer, Jesse G. Urbanowicz, Ryan J. Martin, Patrick C. N. O’Connor, Karen Li, Ruowang Peng, Pei-Chen Bright, Tiffani J. Tatonetti, Nicholas Won, Kyoung Jae Gonzalez-Hernandez, Graciela Moore, Jason H. BioData Min Editorial 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. BioMed Central 2023-07-13 /pmc/articles/PMC10339472/ /pubmed/37443040 http://dx.doi.org/10.1186/s13040-023-00339-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Editorial
Meyer, Jesse G.
Urbanowicz, Ryan J.
Martin, Patrick C. N.
O’Connor, Karen
Li, Ruowang
Peng, Pei-Chen
Bright, Tiffani J.
Tatonetti, Nicholas
Won, Kyoung Jae
Gonzalez-Hernandez, Graciela
Moore, Jason H.
ChatGPT and large language models in academia: opportunities and challenges
title ChatGPT and large language models in academia: opportunities and challenges
title_full ChatGPT and large language models in academia: opportunities and challenges
title_fullStr ChatGPT and large language models in academia: opportunities and challenges
title_full_unstemmed ChatGPT and large language models in academia: opportunities and challenges
title_short ChatGPT and large language models in academia: opportunities and challenges
title_sort chatgpt and large language models in academia: opportunities and challenges
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339472/
https://www.ncbi.nlm.nih.gov/pubmed/37443040
http://dx.doi.org/10.1186/s13040-023-00339-9
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