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Machine yearning: LLMs do not capture formal linguistic structure and obscure neuroscientific inquiry
Elliot Murphy1, Paolo Morosi2, Evelina Leivada2,3
1Vivian L. Smith Department of Neurosurgery, University of Texas Health Science Center at Houston, Houston, USA elliot.murphy@uth.tmc.edu.
The Behavioral and Brain Sciences
|June 30, 2026
Summary
Neural networks show promise in learning language, but they have not achieved true mastery of syntax. Further research is needed to understand their role in the cognitive neuroscience of language.
Area of Science:
- Cognitive Neuroscience
- Computational Linguistics
- Artificial Intelligence
Background:
- Recent advancements suggest neural networks are capable of learning complex linguistic patterns.
- The extent to which these models truly understand syntax remains a subject of debate.
Purpose of the Study:
- To critically evaluate claims that neural networks have mastered language syntax.
- To examine the implications of current neural network capabilities for the cognitive neuroscience of language.
Main Methods:
- Review and analysis of recent empirical evidence on neural network language processing.
- Theoretical argumentation regarding the interpretation of model behavior in relation to linguistic theory.
Main Results:
- Evidence suggests neural networks have learned non-trivial aspects of language.
- However, these systems have not demonstrated "mastery" of syntax.
- Current models obscure rather than clarify cognitive insights into language.
Conclusions:
- Claims of neural network language mastery, particularly in syntax, are premature.
- The interpretability of neural networks remains a significant challenge for cognitive neuroscience.
- Further research should focus on developing more interpretable models and rigorous evaluation methods.
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