作为X数据注释器的GPT-4:解开其在立场分类任务中的性能
Chandreen R Liyanage1, Ravi Gokani2, Vijay Mago3
1Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada.
PloS one
|August 15, 2024
概括
这项研究评估了GPT-4作为社交媒体文本注释器,发现Few-shot和Zero-shot思维链提示方法取得了可比结果. 然而,GPT-4的表现并没有超过对人类标签进行微调以检测姿势的模型.
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.
背景情况:
- 对于NLP的数据注释是昂贵和耗时的,特别是对于社交媒体文本,因为其独特的特点.
- 人类注释者需要广泛的培训,并表现出不同的感知,影响标签一致性.
研究的目的:
- 建立GPT-4作为社交媒体文本注释器用于姿势检测的性能基准.
- 为了比较GPT-4使用零射击,少射击和零射击与思维链提示技术的性能.
主要方法:
- 使用定制标签的推特数据集进行姿态检测.
- 用三种GPT-4提示技术进行了实验:零射击,少数射击和零射击与思维链.
- 微调变压器和传统的机器学习模型在各种标签集上,包括人类标签,用于比较.
主要成果:
- 在GPT-4中,使用Few-shot和Zero-shot的思维链提示实现了类似的结果.
- 没有GPT-4提示技术超过了对人类标签进行微调的模型.
- 零射线思维链被证明对基于方面的社交媒体文本标签有效.
结论:
- GPT-4显示出作为社交媒体文本注释器的潜力,特别是在高级提示策略中.
- 人类标记的数据对于训练高性能姿势检测模型仍然优越.
- 零射击思维链为特定的社交媒体注释任务提供了一个有希望的,高效的替代方案.
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