在文本注释任务中,ChatGPT的表现优于众筹工作者
Fabrizio Gilardi1, Meysam Alizadeh1, Maël Kubli1
1Department of Political Science, University of Zurich, Zurich 8050, Switzerland.
概括
在文本分类任务 (如相关性和主题检测) 中,ChatGPT显著优于人类注释者. 这种人工智能模型提供了更高的准确性和协议,成本很小,彻底改变了自然语言处理应用程序.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 机器学习 机器学习
背景情况:
- 手动文本注释对于培训NLP分类器和评估模型至关重要.
- 任务范围从简单的相关性到复杂的检测,通常由人群工作者或训练有素的注释者执行.
研究的目的:
- 为了比较ChatGPT与人类注释者的性能,用于各种文本注释任务.
- 评估人工智能驱动注释的准确性,代码间协议和成本效益.
主要方法:
- 利用了四个推特和新闻文章 (n = 6,183) 的数据集.
- 评估了ChatGPT在相关性,立场,主题和检测等任务上的零射击性能.
- 将ChatGPT的结果与群众工作者和训练有素的注释者进行比较.
主要成果:
- 聊天GPT的零射击精度超过了群众工作者平均25个百分点.
- 与群众工作者和训练有素的注释者相比,ChatGPT展示了优越的代码间协议.
- 聊天GPT的每注释成本不到0.003美元,比MTurk便宜得多.
结论:
- 像ChatGPT这样的大型语言模型显示了提高文本分类效率的巨大潜力.
- 人工智能驱动的注释为手工方法提供了更准确,更一致,更具成本效益的替代方案.
- 这一进步可能会彻底改变NLP应用程序的开发和评估方式.
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