差异性学习动力学控制了在上下文学习过程中从记忆到概括的过渡.
1Princeton Neuroscience Institute, Princeton University.
ArXiv
|December 23, 2024
概括
在变换器中,上下文学习 (ICL) 从记忆到概括的转变是由于学习速度而不是能力. 一个新的缩放定律预测了这种泛化发生的时间.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 变压器表现出上下文学习 (ICL),适应新信息而无需加权更新.
- ICL出现了多样化的任务培训,显示了从记忆到概括的急剧过渡.
- 假设有限的网络容量驱动了这种泛化转变.
研究的目的:
- 研究ICL中记忆到泛化过渡的机制基础.
- 检查ICL中子电路学习动态的作用.
- 开发一个理论框架来解释ICL现象.
主要方法:
- 在合成ICL任务中使用了一个小型变压器模型.
- 结合了理论分析和实验验证.
- 分析了不同子电路的学习动态.
主要成果:
- 确定了用于记忆和概括的大部分独立子电路.
- 证明了相对学习率,而不是能力,决定了过渡.
- 发现了一个记忆缩放定律,确定了概括值.
- 量化解释 ICL 获取时间,解决方案双模式,以及对数据统计的敏感性.
结论:
- 过渡到ICL泛化是由独立的记忆和泛化子电路的差异性学习速率驱动的.
- 一个新的记忆缩放定律为了解ICL的出现和行为提供了定量基础.
- 开发的理论为各种观察到的ICL现象提供了统一的解释.
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