The Bitter Lesson Strikes Tokenization: A New Era for LLMs?
This post delves into the pervasive 'tokenization' problem in large language models (LLMs) and explores potential solutions. Traditional tokenization methods like Byte-Pair Encoding (BPE), while effective in compressing vocabularies, limit model expressiveness and cause various downstream issues. The article analyzes various architectures attempting to bypass tokenization, including ByT5, MambaByte, and Hourglass Transformers, focusing on the recently emerged Byte Latent Transformer (BLT). BLT dynamically partitions byte sequences, combining local encoders and a global transformer to achieve better performance and scalability than traditional models in compute-constrained settings, particularly excelling in character-level tasks. While BLT faces challenges, this research points towards a new direction for LLM development, potentially ushering in an era free from tokenization.