复杂性控制在变压器中促进了基于推理的组成概括
IEEE transactions on pattern analysis and machine intelligence
|December 19, 2025
概括
变压器模型中的复杂性控制决定了它们是否将推理概括或依赖于记忆. 像初始化尺度和重量衰减这样的策略引导变压器向基于推理或基于内存的解决方案进行构成任务.
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:
- 变压器表现出强的性能,但它们处理组合问题的能力受到争议.
- 了解变压器的内部机制对于提高它们的概括能力至关重要.
研究的目的:
- 在作曲任务中调查变压器的内部机制.
- 确定影响变形金刚学习可概括规则或依赖记忆的因素.
主要方法:
- 应用复杂性控制策略 (参数初始化规模,重量衰减).
- 在信息电路上使用掩盖策略.
- 采用多种复杂度指标来分析内部工作.
- 在图像生成和自然语言处理数据集中验证的发现.
主要成果:
- 复杂性控制显著影响解决方案类型:基于推理 (可概括) 和基于记忆 (可记忆).
- 确定了基于推理和基于记忆的解决方案的明确内部机制.
- 基于推理的解决方案显示出较低的复杂性偏差,与神经元凝聚相关.
结论:
- 复杂性控制是使变压器能够学习构成任务的推理规则的关键.
- 基于推理的解决方案中的较低复杂性偏差被假设为促进规则学习.
- 这些发现在各种现实世界AI任务中广泛适用.
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