Abstract
Across its long intellectual history, from prescriptivism and descriptivism to structuralism and generative grammar, linguistics has the ultimate goal of modelling the internal language faculty of the human mind. Despite the accumulation of extensive body of micro-level findings, a substantial gap persists between such fragmented insights and the construction of a holistic model of human language as a cognitive system. Since the 1950s, the dominant paradigm trajectory in artificial intelligence has evolved from symbolism through empiricism to connectionism. Although contemporary large language models (LLMs), powered by leveraging massive computation and vast datasets, can model human language at unprecedented computational power, they remain limited in capturing the deep cognitive regularities that are not fully encoded in surface distributions and therefore still fall short of native-speaker competence in extracting and internalizing deeper cognitive and semantic regularities that are weakly expressed in surface distributions alone. In this context, it opens a critical space for collaboration. Linguists, drawing on their theoretical sensitivity to subtle semantic, pragmatic, and structural phenomena, can contribute by leveraging domain-specific insight to uncover the deep semantic issues embedded in micro-level linguistic phenomena and to transform linguistic knowledge into high-quality, structured data for improving AI’s linguistic capabilities. At the same time, AI offers linguistics the possibility of moving beyond observational, descriptive, and explanatory adequacy toward generative adequacy. Consequently, the automated transformation of theoretical research into interactive linguistic data—evolving from manual to fully autonomous processes—constitutes a central challenge for linguists in the AI era, and addressing this challenge is essential if linguistics is to play a constitutive role in the next stage of language modelling.
Key words
generative artificial intelligence /
formal grammar /
large language models /
deep learning
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Zhan Weidong.
Rethinking the Integration of Linguistics and Artificial Intelligence Through Paradigm Shifts[J]. Chinese Journal of Language Policy and Planning. 2026, 11(3): 41-52 https://doi.org/10.19689/j.cnki.cn10-1361/h.20260303
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