在L1和L2读者的阅读理解:通过大型语言模型揭示的神经计算机制
Chanyuan Gu1, Samuel A Nastase2, Zaid Zada2
1Department of Language Science and Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China.
NPJ science of learning
|July 10, 2025
概括
大型语言模型 (LLM) 在第一语言 (L1) 和第二语言 (L2) 读者中显示出类似的大脑对齐. 语言和注意力能力的个体差异进一步解释了这两个群体的阅读差异.
科学领域:
- 认知神经科学 认知神经科学
- 计算语言学 计算语言学
- 心理语言学 心理语言学
背景情况:
- 越来越多的证据表明,人类语言理解和大型语言模型 (LLM) 之间存在共享的计算机制.
- 以前的研究主要集中在母语人士身上,限制了对不同语言群体中LLM-人类大脑对齐的理解.
- 调查第一语言 (L1) 和第二语言 (L2) 读者对于全面了解语言处理至关重要.
研究的目的:
- 调查LLM-大脑对齐反映了L1和L2读者之间语言理解的相似性和差异的程度.
- 探索个体因素的影响,如语言和注意力能力,在不同读者群体的LLM-大脑对齐.
- 确定LLM是否可以作为理解不同人群阅读过程的认知可信模型.
主要方法:
- 脑电图 (EEG) 用于记录 L1 和 L2 英语读者在理解文本时的脑反应.
- 阅读表现被评估并与个体差异因子相关联.
- 计算模型,特别是LLM,用于预测大脑活动,允许评估模型-大脑对齐.
主要成果:
- 在群体层面上,L1和L2阅读者在广泛分布的大脑区域中表现出相似的LLM-大脑对齐.
- 来自LLM的上下文嵌入独特地为这两个读者群体的调整做出了贡献.
- 在个人层面上,语言能力影响了L1和L2读者的对齐,而注意力能力和语言主导地位特别影响了L2读者.
结论:
- 在母语和非母语使用者的语言理解任务中,LLM显示出显著的模型-大脑对齐.
- 认知能力的个体差异在塑造LLM-大脑对齐方面发挥着作用,独特的因素影响了第二语言的学习.
- 这些发现支持LLM作为模型人类阅读过程的认知可信工具的实用性,包括跨多种语言背景的共享和独特的神经机制.
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