Study Reveals How Language Models Learn Grammatical Substructure
Researchers analyzed how neural language models learn context-free grammars and their substructures, finding that modeling loss decomposes recursively across grammatical components. The study, accepted to ICML 2026, extends prior work by examining how models internalize grammar substructure during training. Understanding these learning dynamics could improve model design and interpretability in domains governed by formal grammars like natural language syntax and code.
A new arXiv paper accepted to the 43rd International Conference on Machine Learning investigates how language models learn the hierarchical structure of context-free grammars (CFGs), which capture domains including natural language syntax, programming languages, and arithmetic. The researchers define subgrammars and prove theorems showing that language modeling loss recurses linearly over top-level subgrammars, decomposing into losses for irreducible components. Empirically, they find that parametrized models learn subgrammars in parallel rather than sequentially mastering simple structures first, as children do. The work demonstrates that subgrammar pretraining can improve performance for small models relative to grammar size, and alignment analyses show pretraining consistently produces internal representations better reflecting the grammar's substructure. These findings advance understanding of neural language model learning dynamics in formally structured domains.
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- arXiv cs.CLCenter
Unraveling Syntax: Language Modeling and the Substructure of Grammars
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