Enhanced Vocabulary

Enhancing the vocabulary of large language models (LLMs) is a key research area aiming to improve their performance and efficiency across diverse tasks and languages. Current efforts focus on optimizing vocabulary size for different model scales, developing adaptive methods for selecting optimal subsets of domain-specific words, and exploring alternative tokenization strategies beyond subwords, including multi-word entities. These advancements are significant because they directly impact LLM performance on downstream tasks, enabling more accurate and efficient natural language processing in various applications, from machine translation to information retrieval.