Tokenization Algorithm

Tokenization algorithms are crucial for converting human language into numerical representations usable by language models, impacting how these models understand and process text. Current research focuses on improving efficiency (e.g., through optimized linear classification), enhancing cross-lingual fairness (especially for morphologically complex languages via methods like grapheme-based encoding), and developing more effective tokenization strategies for specific applications like recommendation systems (using techniques such as masked vector quantization). These advancements are vital for improving the performance and applicability of language models across diverse languages and tasks, ultimately leading to more robust and equitable natural language processing systems.