Online Tokenizer

Online tokenizers are crucial components of large language models (LLMs), responsible for converting raw text or other data into numerical representations suitable for model processing. Current research focuses on improving tokenizer efficiency (e.g., reducing token length, optimizing vocabulary size), adapting tokenizers for specific languages or domains (e.g., low-resource languages, code, social media), and exploring novel tokenizer architectures (e.g., those based on linear predictive coding or incorporating linguistic features). These advancements are significant because optimized tokenizers directly impact LLM performance, training speed, memory usage, and the ability to effectively handle diverse data types, ultimately improving the efficiency and capabilities of various NLP applications.