What is the Byte Latent Transformer (BLT)?
The Byte Latent Transformer (BLT) is a revolutionary neural network architecture that eliminates the traditional tokenization step in large language models. Instead of converting text into tokens, BLT directly processes raw byte sequences using dynamically-sized patches, enabling models to handle any text format while improving efficiency, robustness to noise, and multilingual performance. BLT matches tokenized LLM performance while offering significant gains in scalability and flexibility.
The quest to build ever-more-powerful Large Language Models (LLMs) is constantly pushing the boundaries of compute, data, and architectural innovation. For years, tokenization has been a seemingly indispensable preprocessing step. But what if we could move beyond it? A new paper introduces the Byte Latent Transformer (BLT), a novel architecture that tackles this challenge head-on. This groundbreaking work demonstrates that you can match the performance of tokenization-based LLMs, while unlocking significant gains in efficiency and robustness at scale. This blog post will delve into the key innovations behind BLT.
The Problem with Tokenization
Traditional LLMs rely on tokenization, a process of grouping raw byte sequences into a predefined, static set of tokens. While effective, this approach introduces several limitations:
- Domain/Modality Sensitivity: Tokenization can bias how strings are compressed, leading to poor generalization across different data types.
- Sensitivity to Input Noise: Small perturbations in the input can lead to vastly different token sequences.
- Lack of Orthographic Knowledge: LLMs struggle with character-level understanding, such as correct spelling or handling of sub-word units.
- Multilingual Inequity: Tokenizers optimized for one language can perform poorly on others, creating biases and inefficiencies.
- Fixed Vocabulary Trade-off: Increasing vocabulary size with tokenization has a trade-off between fewer steps for the model, but also larger embeddings to manage.
The BLT Approach: Dynamic Patching
Instead of static tokens, BLT directly learns from raw byte data using dynamically-sized patches. Here’s how BLT works:
- Byte Encoding: Raw byte sequences are fed into a lightweight Local Encoder module. This module includes key innovations:
- Hash N-Gram Embeddings: BLT captures contextual information by incorporating a series of byte n-gram hash embeddings alongside the byte embeddings, improving the richness of representation at each processing step.
- Cross-Attention Pooling: BLT uses cross-attention with patch representations as queries and byte representations as keys and values, effectively pooling byte data into the variable-sized patches.
- Dynamic Patching: Patches are not static. A learnable patching method groups bytes into patches based on the entropy of the next byte prediction, using a smaller byte-level language model. This allows BLT to allocate compute dynamically, spending more capacity on complex sequences and less on simple ones. There are two entropy methods investigated, a global threshold method, and an approximate monotonicity method which tries to track entropy decreases.
- Latent Transformer: The patches are then fed into the Latent Global Transformer, a large, autoregressive transformer similar to those used in existing LLMs. The global transformer leverages a block-causal attention mask which restricts attention to current patch, and preceding patches.
- Byte Decoding: Finally, the Local Decoder module, another lightweight transformer, transforms patch representations back into a sequence of output bytes using a similar cross attention pooling strategy with the roles of queries, keys and values reversed.
Key Advantages of BLT:
- Efficiency: By dynamically adjusting patch sizes, BLT allocates compute based on data complexity, improving both training and inference speed. Longer patches save compute, which can be reallocated to the global latent transformer, because it is run less often. The paper shows that the resulting models can train with less FLOPs for similar performance as tokenized models.
- Robustness: Direct access to raw bytes allows BLT to generalize better to noisy inputs, learn orthographic rules, and improve low resource language translation. The authors even investigate various noising techniques applied to input data and show improvements over tokenized based models.
- Scalability: Unlike tokenizer-based models, where increasing vocabulary size is expensive and has a limit, the patch-based approach allows for scaling both model and patch sizes within the same inference budget.
- Flexibility: The model can handle arbitrary groups of bytes and does not require a fixed vocabulary.
Scaling Trends and Performance
The paper presents extensive experiments, showing:
- BLT models trained on 4T bytes of data reach parity with the compute-optimal scaling trends of tokenizer-based Llama 3 models up to 8B parameters.
- BLT can be trained with dynamic patch sizes where the average patch is 6 or even 8 bytes compared to the 3.7-4.4 byte average for BPE in Llama 2 and 3 models. This directly leads to savings in inference FLOPs, as the model takes fewer steps per sequence, because the larger transformer is run less often.
- BLT models show significant improvements in modeling the long tail of the data, demonstrating better awareness of character-level structures in language. This was demonstrated through experiments on orthographic knowledge, phonology, and low-resource machine translation tasks.
- Models using an entropy-based dynamic patching method outperformed space-based or static methods.
A New Frontier in LLM Architecture
The Byte Latent Transformer represents a significant step forward for large language model architecture. By moving beyond fixed token vocabularies and embracing a dynamic patching approach, BLT not only matches the performance of current state-of-the-art models but opens the doors to a new era of efficiency, robustness, and scalability. This research is not just a refinement of current techniques, but a paradigm shift that paves the way for a future where LLMs can learn directly from the raw fabric of information.
Key Takeaways
- Tokenization is not a must-have for LLMs
- Dynamic byte patching is a viable alternative
- Models can be trained using raw byte data at scale
- Significant benefits can be made in both efficiency and robustness
- A new method for LLM scaling by dynamically adjusting patch and model size
The Future of BLT
As we continue to push the boundaries of what’s possible with LLMs, the Byte Latent Transformer offers a compelling vision of where the field may be headed. While this research represents a major breakthrough, it’s essential to explore questions around optimal architectural choices at ever larger model scales. The authors have open sourced the training and inference code for BLT at https://github.com/facebookresearch/blt, so that you can delve into the intricacies of the model and experiment with this groundbreaking technology. I hope you found this review helpful in thinking about the next generation of large language models!
Frequently Asked Questions
What is tokenization and why is it problematic?
Tokenization is the process of breaking text into smaller units called tokens before feeding it to language models. While this approach has been standard practice, it introduces several problems: domain sensitivity where tokenizers perform poorly on different data types, sensitivity to input noise where small changes create different token sequences, lack of character-level understanding making spelling difficult, multilingual inequity where some languages get better tokenization than others, and fixed vocabulary trade-offs that limit scalability.
How does BLT differ from traditional tokenized models?
BLT eliminates tokenization entirely by processing raw byte sequences directly. Instead of using a fixed vocabulary of tokens, BLT uses dynamically-sized patches that adjust based on the complexity of the input data. This approach allows BLT to allocate more compute to complex sequences and less to simple ones, improving efficiency while maintaining or improving performance compared to traditional tokenized models like Llama.
What are dynamic patches in BLT?
Dynamic patches are variable-sized groupings of bytes that BLT learns to create automatically based on the entropy (complexity) of the next byte prediction. Unlike static tokens that use fixed boundaries, dynamic patches can grow larger for simple sequences (saving compute) and stay smaller for complex ones (allocating more capacity where needed). This dynamic allocation is key to BLT’s efficiency improvements.
What are the main advantages of BLT over tokenized models?
BLT offers four key advantages: efficiency through dynamic compute allocation based on data complexity, robustness to noisy inputs and better generalization across different data types, scalability without vocabulary size limitations, and flexibility to handle arbitrary byte sequences without fixed vocabularies. These benefits make BLT particularly promising for multilingual applications and noisy real-world data.
How does BLT’s performance compare to traditional LLMs?
BLT models trained on 4T bytes of data reach parity with compute-optimal Llama 3 models up to 8B parameters. They can use average patch sizes of 6-8 bytes compared to 3.7-4.4 bytes for BPE in Llama models, directly reducing inference FLOPs. BLT also shows significant improvements in character-level understanding, orthographic knowledge, phonology, and low-resource machine translation compared to tokenized approaches.
What is hash n-gram embedding in BLT?
Hash n-gram embeddings are a technique BLT uses to capture contextual information at each processing step. Alongside byte embeddings, BLT incorporates a series of byte n-gram hash embeddings—essentially looking at patterns of multiple consecutive bytes—to improve the richness of representation. This helps the model better understand character-level structures and relationships within the byte sequence.
Can I use BLT in my own projects?
Yes, the BLT research team has open-sourced the training and inference code at https://github.com/facebookresearch/blt. You can experiment with the architecture and integrate it into your own projects. However, note that BLT represents research-level architecture—production deployment would require careful consideration of your specific use cases and performance requirements.
About the Author
Vinci Rufus is a technologist and writer analyzing breakthrough developments in AI architecture and their practical implications. He writes about foundational advances like sequence-to-sequence learning and emerging architectures that are shaping the future of large language models. His work focuses on making cutting-edge AI research accessible to practitioners and decision-makers.