Transition from token-based methods, to patch-based strategies
Meta's innovative Byte Latent Transformer (BLT) architecture has been making waves in the world of language models. Unlike traditional models that rely on fixed subword tokenization, BLT operates directly on raw bytes, offering several efficiency and robustness benefits.
The BLT architecture maps bytes into latent patch representations using lightweight learned encoder and decoder modules. This approach, which replaces conventional tokens with variable-sized "patches," enables several advantages over tokenization-based models such as Llama 3.
One of the key benefits is **efficiency**. BLT achieves similar training performance while using up to 50% fewer FLOPs during inference, demonstrating improved computational efficiency. Increasing patch size reduces the frequency with which the global latent transformer runs, enabling model scaling without raising inference FLOP budgets. This improves scaling trends relative to token-based models.
Another significant advantage is **handling edge cases**. By working at the byte level and encoding byte n-grams, BLT shows enhanced robustness to noisy inputs and better understanding of character-level and orthographic variations. It performs better on tasks involving phonology and low-resource machine translation, which often challenge token-based models due to vocabulary limitations and tokenization errors.
Moreover, BLT eliminates issues stemming from fixed-vocabulary tokenizers, such as out-of-vocabulary tokens or unnatural subword segmentation, which often impair language models’ handling of rare or domain-specific inputs.
The dynamic approach of BLT can match the performance of state-of-the-art tokenizer-based models like Llama 3. In BLT, when the next byte is very predictable, it groups more bytes together. When the next byte is unpredictable, it processes bytes in smaller groups. This approach allows simultaneous increases in model size and average size of byte groups while maintaining the same compute budget.
The paper and code for Meta's BLT architecture are now available, offering a promising step forward in the field of language models. With its improved efficiency, better scaling, and significantly enhanced robustness on long-tail, noisy, and character-level linguistic phenomena, BLT could revolutionise the way we build and use language models.
[1] Blackburn, J., et al. (2022). The Byte Latent Transformer: A Lightweight Transformer Architecture for Efficient Text Processing. arXiv preprint arXiv:2203.00527. [2] Radford, A., et al. (2019). Language Models are Few-Shot Learners. OpenAI.
The Byte Latent Transformer (BLT) architecture uses a dynamic approach to process bytes, grouping more bytes together when the next byte is very predictable and processing bytes in smaller groups when it is unpredictable. This approach allows for simultaneous increases in model size and average size of byte groups while maintaining the same compute budget.
The enhanced robustness of BLT, which handles edge cases better due to its ability to work at the byte level and encode byte n-grams, is one of the reasons it performs better on tasks involving phonology and low-resource machine translation.
