弥合儿童与大型语言模型之间的数据差距
1Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, USA.
Trends in cognitive sciences
|September 2, 2023
概括
大型语言模型 (LLM) 需要比儿童学习更多的数据. 这项研究探讨了为什么孩子能够更有效地学习,考虑了先前知识和社交互动等因素.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 发展心理学 发展心理学
背景情况:
- 大型语言模型 (LLM) 显示出新兴的行为,但需要大量的数据.
- 人类儿童在相对较少的数据上获得了显著的语言能力.
- 在LLM和人类学习之间,样本效率存在显著差距.
研究的目的:
- 调查与LLMs相比,儿童语言学习的优异样本效率背后的原因.
- 确定有助于人类高效学习的关键因素.
- 为了帮助开发更有效的样本AI模型.
主要方法:
- 对LLM和人类儿童的数据要求进行比较分析.
- 审查关于儿童发展和人工智能的现有文献.
- 假设学习效率差异的潜在解释.
主要成果:
- 语言学士的语言训练数据比人类儿童的语言数据多1万到10万倍.
- 儿童效率的候选解释包括先前存在的概念知识.
- 多式联络接地和交互性,社会性质的输入也是提出的因素.
结论:
- 数据暴露的巨大差异突出了人工智能研究的关键领域.
- 儿童的学习优势可能源于集成的认知和社会机制.
- 了解这些差异对于推进人工智能至关重要.
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