Vocabulary Size

Vocabulary size in large language models (LLMs) is a critical factor influencing model performance and efficiency, with recent research focusing on optimizing vocabulary size relative to model parameters and available computational resources. Studies using various architectures, including BERT and transformer-based models, demonstrate that larger vocabularies generally improve performance on downstream tasks, but only up to a certain point determined by the model's size and training data. This research is significant because it directly impacts the cost-effectiveness and performance of LLMs across diverse applications, particularly in low-resource language settings where efficient vocabulary expansion is crucial.