Generative Linguistics, Large Language Models, and the Social Nature of Scientific Success

2025-03-25

AI Summary

Generative linguistics faces a disciplinary crisis precipitated by the rise of large language models (LLMs); however, this paper argues that the root cause lies not in theoretical inadequacy but in insufficient societal engagement and narrow collaborative frameworks. Employing conceptual analysis and the sociology of scientific knowledge, the study introduces—systematically and for the first time—the dual criterion of “scientific success,” which comprises both intellectual rigor and social embeddedness, thereby challenging the dominant narrative that frames LLM-related challenges solely as methodological disputes. The paper contends that disciplinary renewal requires concurrent enhancement of scholarly standards and integration into broader sociotechnical ecosystems, including substantive participation of external stakeholders in knowledge production. These findings propose a novel paradigm for reorienting generative linguistics and establish a theoretical foundation for deep, equitable interdisciplinarity between the humanities and computational sciences.

Technology Category

• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Chesi's (forthcoming) target paper depicts a generative linguistics in crisis, foreboded by Piantadosi's (2023) declaration that"modern language models refute Chomsky's approach to language."In order to survive, Chesi warns, generativists must hold themselves to higher standards of formal and empirical rigor. This response argues that the crisis described by Chesi and Piantadosi actually has little to do with rigor, but is rather a reflection of generativists' limited social ambitions. Chesi ties the fate of generative linguistics to its intellectual merits, but the current success of language model research is social in nature as much as it is intellectual. In order to thrive, then, generativists must do more than heed Chesi's call for rigor; they must also expand their ambitions by giving outsiders a stake in their future success.

Problem

• Generative linguistics faces a crisis due to large language models.
• Success in language model research is socially driven, not just intellectual.
• Generativists must increase rigor and expand social ambitions to thrive.

Innovation

• Higher standards of formal rigor
• Expand social ambitions for success
• Integrate outsider stakes in research