Ten simple rules for using large language models in science, version 1.0

作者：Gabriel Reuben Smith, Carolina Bello, Lalasia Bialic‐Murphy, Emily Clark, Camille S. Delavaux, Camille Fournier de Laurière, Johan van den Hoogen, T. Bruce Lauber, Haozhi Ma, Daniel S. Maynard, Matthew Mirman, Lidong Mo, Dominic Rebindaine, Josephine Elena Reek, Leland K. Werden, Zhaofei Wu, Gayoung Yang, Qingzhou Zhao, Constantin M. Zohner, Thomas W. Crowther · 发表于：PLoS Computational Biology · 年份：2024 · DOI：10.1371/journal.pcbi.1011767 · 被引用次数：17 · 研究领域：Artificial Intelligence in Healthcare and Education、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

Box 1-An LLM suggests 10 simple rules governing its own usage in scienceHere, we ask ChatGPT to generate a list of 10 simple rules for using LLMs in science in order to illustrate its ability to provide detailed, human-like responses to short prompts.Q1: Can you provide me with a list of ten simple rules for using large language models in science?R1 (ChatGPT 3.5): Certainly!Here are ten simple rules for using large language models in science:1. Understand the limitations: Recognize that large language models like GPT-3 are powerful but not infallible.They may produce accurate-sounding information, but they can also generate false or biased content.Exercise caution and critical thinking.2. Validate and verify: Always cross-reference information generated by language models with reliable sources.Verify the accuracy of claims, check for