학술논문
Ten simple rules for using large language models in science, version 1.0.
Document Type
Article
Author
Smith, Gabriel Reuben; Bello, Carolina; Bialic-Murphy, Lalasia; Clark, Emily; Delavaux, Camille S.; Fournier de Lauriere, Camille; van den Hoogen, Johan; Lauber, Thomas; Ma, Haozhi; Maynard, Daniel S.; Mirman, Matthew; Mo, Lidong; Rebindaine, Dominic; Reek, Josephine Elena; Werden, Leland K.; Wu, Zhaofei; Yang, Gayoung; Zhao, Qingzhou; Zohner, Constantin M.; Crowther, Thomas W.
Source
Subject
*LANGUAGE models
*GENERATIVE artificial intelligence
*SCIENTIFIC language
*GENERATIVE pre-trained transformers
*LINGUISTICS
*CHATBOTS
*TUNDRAS
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Language
ISSN
1553-734X
Abstract
The article explores the use of large language models (LLMs) in scientific research, highlighting their potential benefits and limitations. It provides examples of how LLMs can assist in various scientific tasks, such as summarizing documents and improving writing. However, it also acknowledges the need for fact-checking and the potential biases in training data. The authors emphasize the importance of adhering to ethical guidelines and journal policies when using LLMs. While LLMs can be valuable tools, caution should be exercised in their use. [Extracted from the article]