在基于变压器的语言模型和人类大脑的共同功能专业化
Sreejan Kumar1, Theodore R Sumers2, Takateru Yamakoshi3
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA. sreejank@princeton.edu.
Nature communications
|July 1, 2024
概括
研究人员分析了语言处理期间的大脑活动,专注于人工神经网络计算. 他们发现"变形金刚"中的特定"注意力头"预测了不同皮层区域的大脑活动,揭示了大脑如何处理复杂的语言.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 大脑通过专门的计算来处理语言,从复杂的结构中获得意义.
- 人工神经网络,特别是变压器,通过通过电路计算集成上下文信息来进行先进的自然语言处理.
- 以前的研究集中在内部表示 (嵌入式),但这项研究直接检查电路计算.
研究的目的:
- 将变压器计算分解为功能专业化转换,集成上下文信息.
- 研究这些转换和个人的注意力如何与自然语言理解过程中的大脑活动有关.
- 在皮层语言网络中映射注意力头的功能专业化.
主要方法:
- 从参与者听到自然主义故事的功能性MRI (fMRI) 数据的分析.
- 将变压器计算分解为功能专业化转换.
- 大脑活动与个别注意力负责人执行的计算的相关性.
主要成果:
- 变压器转换解释了皮层语言网络中大脑活动的显著差异.
- 个别的,功能专业化的注意力头不同预测特定皮质区域的大脑活动.
- 关注头在一个低维的皮质空间中表现出与网络层和上下文长度相关的梯度.
结论:
- 这项研究通过直接分析电路计算,提供了对大脑语言处理的新计算解释.
- 人工神经网络中的新兴计算,特别是注意力头,为语言理解的神经机制提供了洞察力.
- 这项研究将人工智能与神经科学联系在一起,为了解大脑如何整合语言背景提供了一个框架.
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