mlsanityData to 14 Oct 2026

Large Language Models as Proxies for Theories of Human Linguistic Cognition

arXiv 2502.07687 ↗First posted 11 Feb 2025cs.CLcited by 10

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Abstract (arXiv)

We consider the possible role of current large language models (LLMs) in the study of human linguistic cognition. We focus on the use of such models as proxies for theories of cognition that are relatively linguistically-neutral in their representations and learning but differ from current LLMs in key ways. We illustrate this potential use of LLMs as proxies for theories of cognition in the context of two kinds of questions: (a) whether the target theory accounts for the acquisition of a given pattern from a given corpus; and (b) whether the target theory makes a given typologically-attested pattern easier to acquire than another, typologically-unattested pattern. For each of the two questions we show, building on recent literature, how current LLMs can potentially be of help, but we note that at present this help is quite limited.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 19 of its 68 references that are in the map. First posted on arXiv 11 Feb 2025.

Builds on

19 of 68 references.

References · 19

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7 more
  1. 2021Minimum Description Length Recurrent Neural Networkscited by 24
  2. 2023How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speechcited by 22
  3. 2024Kallini et al. (2024) do not compare impossible languages with constituency-based onescited by 15
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.