使用 GPT-4 撰写科学评论文章:试点评估研究。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-06-18 DOI:10.1186/s13040-024-00371-3
Zhiping Paul Wang, Priyanka Bhandary, Yizhou Wang, Jason H Moore
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引用次数: 0

摘要

作为 OpenAI 大型语言模型的最高级版本,GPT-4 已引起广泛关注,并迅速成为各个领域不可或缺的人工智能工具。这包括科学家们对其在不同应用领域的探索。我们的研究重点是评估 GPT-4 为生物医学综述论文生成文本、表格和图表的能力。我们还评估了 GPT-4 生成文本的一致性,以及使用该模型撰写科学评论论文时可能存在的抄袭问题。基于这些结果,我们建议开发 ChatGPT 的增强功能,以更有效地满足科学界的需求。这包括加强对参考资料上传文档的处理,更深入地掌握复杂的生物医学概念,更精确、更高效地提炼信息以生成表格,以及进一步完善专门用于科学图表创建的模型。
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Using GPT-4 to write a scientific review article: a pilot evaluation study.

GPT-4, as the most advanced version of OpenAI's large language models, has attracted widespread attention, rapidly becoming an indispensable AI tool across various areas. This includes its exploration by scientists for diverse applications. Our study focused on assessing GPT-4's capabilities in generating text, tables, and diagrams for biomedical review papers. We also assessed the consistency in text generation by GPT-4, along with potential plagiarism issues when employing this model for the composition of scientific review papers. Based on the results, we suggest the development of enhanced functionalities in ChatGPT, aiming to meet the needs of the scientific community more effectively. This includes enhancements in uploaded document processing for reference materials, a deeper grasp of intricate biomedical concepts, more precise and efficient information distillation for table generation, and a further refined model specifically tailored for scientific diagram creation.

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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.
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