一个共享的基于模型的语言空间,用于在自然对话中将我们的想法从大脑传输到大脑
Zaid Zada1, Ariel Goldstein2, Sebastian Michelmann1
1Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544, USA.
Neuron
|August 3, 2024
概括
研究人员在对话期间绘制了大脑活动的地图,揭示了大型语言模型 (LLM) 如何从数值上模拟共享意义. 这种脑对脑的语言信息交换是逐字进行的,增强了沟通理解.
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
- 认知科学 认知科学
背景情况:
- 有效的沟通依赖于对单词含义的共同理解.
- 追踪自然对话背后的神经过程是具有挑战性的.
研究的目的:
- 在自发对话中模拟语言信息交换的神经动态.
- 调查大型语言模型 (LLM) 是否能够捕捉人类沟通中共享的含义空间.
主要方法:
- 在自然对话期间记录了患者的电皮质谱 (ECoG) 脑活动.
- 开发了一个基于模型的合框架,将扬声器和听众的大脑活动与LLM嵌入方式对齐.
- 追踪了大脑之间逐字的语言信息交换.
主要成果:
- 情境敏感的LLM嵌入成功地调整了对话个体之间的神经活动.
- 语言内容在发言者大脑中出现于表达前,并在表达后在听者的大脑中重新出现.
- 与语法和表达模型相比,LLM的上下文嵌入提供了优越的逐词神经对齐.
结论:
- 法学士课程的上下文嵌入提供了人类沟通中使用的共享,丰富的上下文含义空间的数值模型.
- 这种框架使得在自然对话过程中可以跟踪神经信息流.
- 这些发现有助于我们更好地理解语义处理和大脑间通信的神经基础.
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