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!