Larger Vocabulary

Research on larger vocabularies in large language models (LLMs) focuses on improving model performance and robustness by expanding the range of words and phrases they can understand and generate. Current efforts investigate the impact of vocabulary size on various tasks, including text classification, machine translation, and speech recognition, often employing techniques like positional encoding and dynamic vocabulary expansion within existing architectures such as transformers and recurrent neural networks. This research is crucial for enhancing the accuracy and reliability of LLMs across diverse applications, particularly in low-resource languages and domains with evolving terminology, ultimately improving the accessibility and utility of these powerful tools.