Arxiv paper - Byte Latent Transformer: Patches Scale Better Than Tokens

In this episode, we discuss Byte Latent Transformer: Patches Scale Better Than Tokens by Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srinivasan Iyer. The Byte Latent Transformer (BLT) presents a novel approach to large language models by processing data at the byte level, eliminating the need for traditional tokenization. It maintains performance comparable to tokenization-based models while offering improvements in efficiency, robustness, and scaling capability. BLT's dynamic encoding of bytes into variable-sized patches allows more efficient utilization of computational resources and successful scaling to larger model sizes, showcasing its potential in handling raw byte data without a fixed vocabulary.