Grammaticality is linearly decodable from language model sentence representations and generalizes across phenomena and languages in larger models.
Generative Linguistics, Large Language Models, and the Social Nature of Scientific Success
1 Pith paper cite this work. Polarity classification is still indexing.
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.
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cs.CL 1years
2026 1verdicts
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Linear representations of grammaticality in neural language models
Grammaticality is linearly decodable from language model sentence representations and generalizes across phenomena and languages in larger models.