Gotcha GPT:确保学术写作中的诚信
João Gabriel Gralha1, André Silva Pimentel1
1Departamento de Química, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ 22453-900, Brazil.
Journal of chemical information and modeling
|October 22, 2024
概括
本研究介绍了一种方法,可以使用机器学习分类器检测人工智能 (AI) 生成的学术写作. 开发的模型具有很高的准确性,有助于保持学术出版物的学术完整性.
科学领域:
- 计算机科学 计算机科学
- 自然语言处理自然语言处理.
- 学术出版学术出版公司
背景情况:
- 人工智能 (AI) 的快速发展给确保学术写作的完整性带来了挑战.
- 对大学和出版商来说,区分人工智能生成和人类生成的手稿至关重要.
- 现有的方法可能不足以解决AI在文本生成方面的不断增长的能力.
研究的目的:
- 开发和评估机器学习模型,以区分人工智能生成和人类生成的学术文本.
- 为学者和出版商提供可靠的工具来验证手稿的作者身份.
- 为在人工智能时代保持学术诚信提供实际解决方案.
主要方法:
- 使用分类器模型,包括决策树,随机森林,额外树和AdaBoost.
- 雇员 Scikit学习图书馆用于统计评估 (精度,准确性,回忆,F1,MCC,科恩的卡帕) 和混矩阵分析.
- 在一个数据集上训练和测试模型,大约有400个人工智能生成的和400个人类生成的科学手稿文本,随机分成50/50.
主要成果:
- 对分类的模型评估准确度在0.97到0.99.9之间.
- 使用的统计指标和混矩阵为模型的性能提供了高度的信心.
- 这些模型显示出强大的区分人工智能和人类生成文本的能力.
结论:
- 开发的分类器模型在识别人工智能生成的学术写作方面具有高准确性.
- 这种方法为保护学术出版物的学术完整性提供了一个有价值的工具.
- 免费可用的教程和代码 (Gotcha GPT) 支持这些方法的实际应用.
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