GENERATIVE LINGUISTICS IN CRISIS: CAN IT SURVIVE THE AGE OF AI?

Аннотация

The rapid advancement of Artificial Intelligence, particularly in natural language processing, has sparked renewed debates about the future of generative linguistics. Traditionally grounded in rule-based, competence-oriented models of language, generative linguistics now faces significant challenges from data-driven, performance-based AI systems such as large language models. This paper explores the core principles of generative grammar, contrasts them with the methodologies employed by AI-driven language technologies, and examines the points of tension between these paradigms. It further investigates whether generative linguistics can adapt, coexist, or potentially integrate with AI advancements, or if it risks becoming obsolete in an era increasingly defined by empirical, computational approaches to language. Through critical analysis, the paper aims to assess the current status of generative theory and envision possible futures for its survival and relevance in modern linguistic research. Keywords: artificial intelligence, generative linguistics, language models, natural language processing, universal grammar, computational linguistics, syntax, data-driven approach, linguistic theory. Generative linguistics is a school of thought within linguistics that makes use of the concept of a generative grammar. The term "generative grammar" is used in different ways by different people, and the term "generative linguistics" therefore has a range of different, though overlapping, meaning. In linguistics, generative is used to describe linguistic theories or models which are based on the idea that a single set of rules can explain how all the possible sentences of a language are formed. While classical linguistic frameworks, such as generative grammar, emphasize abstract rules and innate structures, modern AI systems rely on large-scale corpora and statistical patterns to understand and generate language. This transformation has led to new methods of linguistic analysis, where machine learning models like GPT or BERT can perform tasks such as translation, summarization, and syntactic parsing with remarkable accuracy—often without explicit grammatical instruction. As a result, linguists are now re-evaluating long-held assumptions about language acquisition, competence, and performance, prompting both opportunities and challenges in integrating AI tools into linguistic research. The rapid rise of Artificial Intelligence ~ 72 ~ “Global lingvistika: yangi yondashuvlar va tadqiqotlar” mavzusidagi xalqaro ilmiy-amaliy anjuman (AI) language models has significantly influenced the field of linguistics, marking a shift from traditional rule-based theories to data-driven approaches. While classical linguistic frameworks, such as generative grammar, emphasize abstract rules and innate structures, modern AI systems rely on large-scale corpora and statistical patterns to understand and generate language. This transformation has led to new methods of linguistic analysis, where machine learning models like GPT or BERT can perform tasks such as translation, summarization, and syntactic parsing with remarkable accuracy—often without explicit grammatical instruction. As a result, linguists are now re-evaluating long-held assumptions about language acquisition, competence, and performance, prompting both opportunities and challenges in integrating AI tools into linguistic research. Generative linguistics, deeply rooted in Noam Chomsky’s theory of Universal Grammar, has been a cornerstone of linguistic theory since its inception in the mid- 20th century. The framework posits that the ability to acquire language is an innate feature of the human mind, governed by universal principles shared across all languages. However, the rapid evolution of artificial intelligence (AI), particularly in the realm of large language models, has sparked debates over the validity and sustainability of generative principles in modern linguistic research. The question now arises: can generative linguistics maintain its theoretical dominance, or is it at risk of obsolescence in the age of data-driven models? Generative linguistics operates on the premise that all human languages share a common structural foundation, known as Universal Grammar. According to Chomsky (1965), this framework is hardwired into the brain, allowing individuals to generate infinite expressions from a finite set of grammatical rules. This perspective emphasizes syntax as the core component of language, distinct from mere vocabulary learning. The theory has shaped decades of linguistic research, providing a structured approach to understanding language acquisition and processing. The emergence of powerful AI language models like GPT-4 has fundamentally challenged long-held beliefs in generative linguistics. These models, trained on vast amounts of textual data, demonstrate the ability to produce coherent and contextually appropriate language without explicit grammatical instruction. U