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Large Language Models as Modal Models in Linguistics

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Computer Science > Computation and Language

arXiv:2606.10467 (cs)
[Submitted on 9 Jun 2026]

Title:Large Language Models as Modal Models in Linguistics

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Abstract:The rapid advancement of large language models (LLMs) has intensified debates about their significance for linguistic theory. These debates are commonly divided into three positions: insulationism, which regards LLMs as irrelevant to human language; eliminativism, which claims that LLMs can replace traditional linguistic theories; and conciliationism, which views them as useful tools for linguistic research. To clarify these positions, this paper applies the framework of modal modeling from the philosophy of science. We argue that LLMs possess genuine epistemic value as minimal models, even without structural correspondence to human cognition. In particular, they can provide how-possibly explanations (HPEs) by testing modal claims about language acquisition and linguistic competence. We then examine the conditions under which LLMs could qualify as how-actually explanations (HAEs) of human language, drawing on the mechanistic account of scientific explanation. We argue that current LLMs do not yet satisfy these requirements. On the basis of this analysis, we propose understanding the explanatory power of LLMs as lying on a continuum between HPEs and HAEs. This framework avoids both overstating and understating their explanatory significance and offers a more precise basis for evaluating the role of LLMs in the scientific study of language.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2606.10467 [cs.CL]
  (or arXiv:2606.10467v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2606.10467
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Haruto Suzuki [view email]
[v1] Tue, 9 Jun 2026 06:37:24 UTC (815 KB)
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