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

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arxiv 2503.20088 v1 pith:JBZ53VV7 submitted 2025-03-25 cs.CL

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

classification cs.CL
keywords chesilanguagegenerativegenerativistslinguisticsmustrigorsocial
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 1 Pith paper

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  1. Linear representations of grammaticality in neural language models

    cs.CL 2026-07 conditional novelty 5.0

    Grammaticality is linearly decodable from language model sentence representations and generalizes across phenomena and languages in larger models.