Unraveling Syntax: Language Modeling and the Substructure of Grammars

This research extends neural language modeling to investigate the relationship between language models and context-free grammar (CFG) substructures, termed subgrammars. The authors establish foundational theorems linking language modeling loss to these subgrammars, demonstrating that parametrized models can learn subgrammars in parallel and that subgrammar pretraining enhances performance in smaller models. This work is significant for practitioners as it provides insights into how language models can better capture grammatical structures, potentially improving model design and training strategies.