This study evaluates the effect of incorporating sub-word units (morphemes) in training large language models (LLMs), highlighting its role in reducing vocabulary size and managing unknown and compound words more effectively. Beyond these established benefits, the research also emphasises the importance of considering the hierarchical morphological structures of languages and their generative capacities. It addresses how simplistic assumptions underpinning syntactic competence, which focus solely on surface linear strings, struggle to resolve　linguistic problems such as ambiguities (e.g., un-lockable versus unlock-able) and long-dependencies (e.g., en-joy-able versus *joy-able). The research posits that an improved understanding of morphological structures, grounded in the Hierarchical Morphological Processing Hypothesis, can enhance the performance and accuracy of LLMs.