A review of “Byte Latent Transformer: Patches Scale Better Than Tokens” by Artidoro Pagnoni et al.
The Byte Latent Transformer (BLT) introduces a groundbreaking approach to large language models (LLMs) by replacing traditional tokenization with a new concept called patches. This innovation challenges the long-standing reliance on token-based architectures and offers significant improvements in efficiency, scalability, and robustness.
Tokens vs. Patches: What’s the Difference?
Tokens:
- Tokens are fixed units of text (like words or subwords) created during a preprocessing step called tokenization.
- Tokenization compresses text into a static vocabulary, which the model uses to process and understand language.
However, tokenization has limitations:
- Bias: Tokenization can introduce biases, especially in multilingual contexts.
- Rigidity: Tokens are fixed, meaning the model cannot dynamically adjust to the complexity of the input.
- Noise Sensitivity: Token-based models struggle with noisy or unconventional inputs.
Patches:
- Patches are dynamic groupings of raw bytes (the smallest units of digital text) created during training.
- Unlike tokens, patches are not predefined. Instead, they are segmented based on the entropy (complexity) of the data.
This means:
- Dynamic Allocation: BLT allocates more computational resources to complex parts of the input and less to simpler parts.
- Flexibility: Patches adapt to the structure of the data, making them more robust to noise and better at handling diverse languages.
- Efficiency: Larger patches reduce the number of steps required for processing, saving computational resources.
How Does BLT Work?
BLT introduces a three-part architecture:
1. Local Encoder: Groups bytes into patches based on their entropy.
2. Global Latent Transformer: Processes the patches to understand the overall context.
3. Local Decoder: Converts the patches back into bytes for output.
This architecture allows BLT to dynamically adjust patch sizes, making it more efficient than token-based models. For example:
– Predictable data (like repeated patterns) can be grouped into larger patches.
– Complex data (like rare words) gets smaller patches for detailed processing.
Why Is BLT Groundbreaking?
- Efficiency: BLT uses up to 50% fewer FLOPs (floating-point operations) during inference compared to token-based models like LLaMA 3.
- Scalability: BLT can scale both model size and patch size simultaneously, unlocking new possibilities for large-scale language modeling.
- Robustness: BLT outperforms token-based models in handling noisy inputs, rare words, and multilingual tasks.
- Fairness: By eliminating tokenization, BLT avoids biases introduced by static vocabularies.
Why Does This Matter?
The shift from tokens to patches represents a paradigm change in how LLMs process language. By working directly with raw bytes, BLT eliminates the need for tokenization, a step that has been a bottleneck in terms of efficiency and fairness. This innovation opens the door to more adaptable, scalable, and robust language models.
Reference
This article is based on the research paper “Byte Latent Transformer: Patches Scale Better Than Tokens” by Artidoro Pagnoni et al., published by FAIR at Meta.
For more details, visit the official code repository: https://github.com/facebookresearch/blt
Review of Byte Latent Transformer by Shamsher Haider originally published on shamshserhaider.com