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在牙科中生成科学摘要的生成预训练变压器的性能:一个比较的观察性研究
Caio Alencar-Palha1, Thais Ocampo1, Thaisa Pinheiro Silva1
1Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, São Paulo, Brazil.
概括
生成式预训练变压器 (GPT) 模型在生成牙科放射学摘要方面表现有前途,这些摘要与人类写作无法区分. 然而,人工智能检测工具可以识别GPT生成的内容,揭示其人工来源.
科学领域:
- 牙科放射学 牙科放射学
- 科学中的人工智能.
背景情况:
- 生产预训练变压器 (GPT) 模型越来越多地用于科学写作.
- 评估GPT在生成科学摘要方面的有效性对于学术诚信至关重要.
研究的目的:
- 评估GPT在牙科领域创建科学摘要方面的表现.
- 为了比较人类和人工智能生成的摘要的可检测性和质量.
主要方法:
- 收集了十篇牙科放射学文章的原始摘要.
- 根据文章的方法和结果,使用ChatGPT生成了10个新的摘要.
- 五位评价员对摘要进行了准确性,格式化和写作质量的评估.
- 人工智能检测工具评估了"人类分数"和抄袭水平.
主要成果:
- 人类评估人员发现,GPT生成的摘要与人类撰写的摘要相当.
- 人工智能检测工具识别了GPT生成的摘要,平均"人类分数"为16.9%.
- 拼写和标点是人工智能生成摘要的关键指标.
结论:
- GPT在生成人类难以区分的科学摘要方面表现出强大的能力.
- 人工智能检测工具在识别GPT生成的内容方面是有效的,这凸显了透明度的必要性.
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