大型语言模型减少了在线问答平台上公众的知识共享
R Maria Del Rio-Chanona1,2,3, Nadzeya Laurentsyeva4,5, Johannes Wachs6,7,3
1Department of Computer Science, University College London, London WC1E 6EA, United Kingdom.
PNAS nexus
|September 25, 2024
概括
像ChatGPT这样的大型语言模型 (LLM) 正在减少像Stack Overflow这样的平台上的人为生成的内容. 数据的减少可能会阻碍未来人工智能模型的开发.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 大型语言模型 (LLM) 提供了人类生成数据的替代品的潜力.
- 越来越多地使用LLM可能会减少对培训未来AI模型至关重要的人类生成内容的可用性.
研究的目的:
- 调查大型语言模型 (LLM),特别是ChatGPT对人类生成内容生产的影响.
- 为了量化ChatGPT发布后堆溢出活动的减少.
主要方法:
- 在ChatGPT发布后记录了堆溢出活动的减少.
- 使用的反事实比较Stack Overflow与受ChatGPT影响较小的资源 (例如,俄罗斯/中国同行,数学论坛).
- 分析了用户体验层次的后质量和内容创建的变化.
主要成果:
- 在ChatGPT发布后的六个月内,堆溢出活动相对于未受影响的平台而言下降了25%.
- 与流行的编程语言相关的帖子下降更为明显.
- 没有观察到后质量的显著变化;所有用户体验级别的内容创建都下降了.
结论:
- 像ChatGPT这样的LLM不仅取代了低质量或初学者内容,还取代了经验丰富的用户的内容.
- 广泛采用LLM正在减少培训未来AI模型所必需的公共数据的产生,这带来了重大挑战.
- 这种培训数据的减少对人工智能开发的持续进步具有关键意义.
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