Large Language Models as Proxies for Theories of Human Linguistic Cognition
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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
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Built on by
Citing papers per year: 2025: 6; 2026: 4.
10 papers.
- 2025Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossiblecited by 3
- 2025Language models as tools for investigating the distinction between possible and impossible natural languagescited by 0
- 2025Grammaticality Judgments in Humans and Language Models: Revisiting Generative Grammar with LLMscited by 0
- 2025Large language models have learned to use languagecited by 0
- 2025Large language models are not about natural languagecited by 0
- 2025Linguists should learn to love speech-based deep learning modelscited by 0
- 2026Are Language Models Models?cited by 0
- 2026Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilitiescited by 0
- 2026You Can't Fight in Here! This is BBS!cited by 0
- 2026Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appearcited by 0
- 2026Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear11 May 2026
- 2026You Can't Fight in Here! This is BBS!10 Apr 2026
- 2026Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities10 Apr 2026
- 2026Are Language Models Models?15 Jan 2026
- 2025Linguists should learn to love speech-based deep learning models16 Dec 2025
- 2025Large language models are not about natural language15 Dec 2025
- 2025Large language models have learned to use language13 Dec 2025
- 2025Grammaticality Judgments in Humans and Language Models: Revisiting Generative Grammar with LLMs11 Dec 2025
- 2025Language models as tools for investigating the distinction between possible and impossible natural languages10 Dec 2025
- 2025Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible8 Oct 2025