将大脑排列在一个共享空间中,可以改善它们与大型语言模型的对齐
Arnab Bhattacharjee1, Zaid Zada2, Haocheng Wang2
1Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA. arnab@princeton.edu.
Nature computational science
|November 19, 2025
概括
这项研究表明,共享大脑活动空间可以改善语言处理过程中大语言模型对神经反应的预测. 这种方法可以提高个体间的编码准确性和通用性.
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
- 机器学习 机器学习
背景情况:
- 大型语言模型 (LLM) 可以预测自然语言处理 (NLP) 期间的神经活动.
- 在个人参与者中评估LLM编码模型的先前方法限制了概括性.
- 电皮质谱 (ECoG) 提供高分辨率的神经数据,用于研究大脑活动.
研究的目的:
- 利用LLMs开发一种更可通用的方法来预测神经活动.
- 为了提高基于LLM的神经编码模型的准确性.
- 研究神经对语言反应中的跨参与者共同点.
主要方法:
- 分析了八名参与者听同一个播客的ECoG数据.
- 开发并应用一个共享的响应模型来估计参与者之间的共同信息空间.
- 在个人和共享空间内评估基于LLM的编码模型性能.
主要成果:
- 一个共享响应模型显著提高了基于LLM的神经编码准确度的37% (从r=0.188到r=0.257).
- 共同的空间使得个人的大脑反应可以被拒绝.
- 在语言专业的大脑区域观察到的最实质性的改善,包括上旋和下额头旋.
结论:
- 估计共享的神经信息空间可以提高基于LLM的编码模型的概括性和准确性.
- 这种方法为解码与语言理解相关的大脑活动提供了一个有希望的方法.
- 研究结果表明,在不同个体之间存在共同的神经语言表征.
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