Tokenization Matter

Tokenization, the process of breaking down text or images into smaller units for machine processing, is crucial for the performance of large language models (LLMs) and vision transformers. Current research focuses on developing linguistically-aware and language-independent tokenization methods, improving efficiency through techniques like superpixel tokenization and optimized linear classification, and addressing inconsistencies that can lead to errors in downstream tasks. These advancements are vital for enhancing the accuracy, efficiency, and inclusivity of AI systems across diverse languages and applications, including image generation, medical image analysis, and natural language processing.