Byte Latent Transformer: Patches Scale Better Than Tokens
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
2024-12-17
Summary
This paper introduces the Byte Latent Transformer (BLT), a new type of language model that works directly with raw bytes instead of using fixed tokens. This approach improves efficiency and performance, especially when dealing with complex data.
What's the problem?
Traditional language models use a method called tokenization, where text is broken down into fixed pieces or tokens. This can lead to inefficiencies and limitations, especially when the data is complex or noisy. These models often struggle to process data effectively, which can hinder their performance in real-world applications.
What's the solution?
The researchers developed BLT, which encodes bytes into flexible groups called patches based on the complexity of the data. This means that the model can allocate more computing resources to parts of the data that are more complicated, improving its ability to understand and generate language. By using this dynamic patching system, BLT achieves better performance without needing a fixed vocabulary or retraining models.
Why it matters?
The significance of BLT lies in its ability to handle language processing more efficiently and robustly than traditional models. By improving how models scale and perform with complex data, BLT could lead to advancements in various applications, from natural language understanding to machine translation, making technology more effective and accessible.
Abstract
We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it. We present the first FLOP controlled scaling study of byte-level models up to 8B parameters and 4T training bytes. Our results demonstrate the feasibility of scaling models trained on raw bytes without a fixed vocabulary. Both training and inference efficiency improve due to dynamically selecting long patches when data is predictable, along with qualitative improvements on reasoning and long tail generalization. Overall, for fixed inference costs, BLT shows significantly better scaling than tokenization-based models, by simultaneously growing both patch and model size.