聊天GPT是否提高了全球卫生出版物的公平性? 通过ChatGPT进行复制编辑,与Grammarly和人类编辑器相比
Ella August1, Rachel Gray2, Zaria Griffin3
1Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.
PloS one
|February 5, 2026
概括
像U-M GPT这样的大型语言模型 (LLMs) 为全球卫生研究人员提供了成本效益高的替代英语复制编辑的替代方案. 然而,由于潜在的不准确性和内容限制,必须仔细审查.
科学领域:
- 医学和健康科学医学和健康科学
- 研究中的人工智能.
- 学术出版学术出版公司
背景情况:
- 由于成本高昂,英语复制编辑对全球卫生作者,特别是低收入国家的作者构成重大障碍.
- 大型语言模型 (LLM) 正在成为学术写作支持的潜在成本效益替代方案.
研究的目的:
- 将生成性AI工具 (U-M GPT) 对 Grammarly 和人类编辑器进行的校正的有效性和质量进行比较.
- 评估LLMs在提高全球卫生研究人员学术出版的公平性和效率方面的潜力.
主要方法:
- 进行了初步的,深入的案例比较.
- 分析了乌干达性和生殖健康研究人员的两篇论文草案的文本.
- 来自U-M GPT,Grammarly和人类编辑器的纠正得到了定量和定性评估.
主要成果:
- 总的来说,U-M GPT的校正量大约是人类编辑器的三倍,是Grammarly的十倍.
- 只有61%的U-M GPT的纠正被认为是改进,这表明需要仔细审查.
- 在速度和成本方面,LLM显示出优势,但对快速工程,数据隐私和敏感主题的内容调节提出了担忧.
结论:
- 虽然LLM在学术出版业增强公平性和生产力方面表现有前途,但其使用需要仔细考虑准确性,偏见和道德影响.
- 需要进一步的研究来验证这些初步发现.
- 建议作者查阅期刊指南,并披露他们在工作中使用LLM.
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