生成语法为人工智能提供核心理论支撑，推动语言模型与认知机制研究。
Generative linguistics contribution to artificial intelligence: Where this contribution lies?
- 从句法、普遍语法等生成语法视角解析语言计算机制
- 揭示人类语言能力与大模型训练的深层关联，支持其有效性
- 适合对语言认知、AI原理感兴趣的学者与研究者
本文旨在厘清生成语言学（GL）对人工智能（AI）的贡献，回应语言学属于人文学科还是科学的争议。以独立科学视角审视，文章系统梳理了来自乔姆斯基学派的理论——包括句法、语义、语言官能、普遍语法、语言计算系统、语言习得、人脑机制、编程语言（如Python）、大语言模型及非偏见AI科学家的研究证据，表明生成语言学对人工智能的贡献极为深远且不可否认。尽管如此，两者在语言输入的本质与类型上仍存在分歧。
原文摘要 · Abstract (English)
This article aims to characterize Generative linguistics (GL) contribution to artificial intelligence (AI), alluding to the debate among linguists and AI scientists on whether linguistics belongs to humanities or science. In this article, I will try not to be biased as a linguist, studying the phenomenon from an independent scientific perspective. The article walks the researcher/reader through the scientific theorems and rationales involved in AI which belong from GL, specifically the Chomsky School. It, thus, provides good evidence from syntax, semantics, language faculty, Universal Grammar, computational system of human language, language acquisition, human brain, programming languages (e.g. Python), Large Language Models, and unbiased AI scientists that this contribution is huge, and that this contribution cannot be denied. It concludes that however the huge GL contribution to AI, there are still points of divergence including the nature and type of language input.
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