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Studies with impossible languages falsify LMs as models of human language
Jeffrey S Bowers1, Jeff Mitchell2
1School of Psychology and Neuroscience, University of Bristol, Bristol, UK j.bowers@bristol.ac.uk.
The Behavioral and Brain Sciences
|June 30, 2026
Summary
Infants and language models (LMs) learn languages differently. While humans prefer natural structures, LMs struggle with complex or random "impossible languages" due to missing human inductive biases.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Developmental Psychology
Background:
- Human infants and language models (LMs) exhibit varying language acquisition capabilities.
- The concept of
Purpose of the Study:
- To investigate the learning capabilities of language models (LMs) compared to human infants.
- To analyze the factors influencing the learnability of attested versus
Main Methods:
- Literature review comparing studies on infant language acquisition and LM performance.
- Analysis of LM performance on linguistic tasks involving natural and unnatural language structures.
Main Results:
- Language models (LMs) often learn attested and many "impossible" languages with equal proficiency.
- LMs do not inherently find unnatural language structures more difficult than attested ones.
- Complexity and randomness, not unnaturalness, appear to be key factors in LM learning difficulty.
Conclusions:
- Language models (LMs) lack the human inductive biases crucial for natural language acquisition.
- The learning mechanisms of LMs differ significantly from those of human infants.
- Further research is needed to imbue LMs with human-like language learning capabilities.
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