{"id":19,"url":"https://pm.philipcastiglione.com/papers/19.json","title":"Byte Latent Transformer: Patches Scale Better Than Tokens","read":false,"authors":"Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Y, Jason Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srinivasan Iyer","year":2024,"auto_summary":"The paper introduces the Byte Latent Transformer (BLT), a novel architecture for large language models (LLMs) that operates at the byte level without relying on traditional tokenization. This approach allows BLT to dynamically allocate computational resources by grouping bytes into patches based on data complexity, thereby improving efficiency and robustness. The study demonstrates that BLT can match the performance of tokenization-based models like Llama 3 at scales up to 8 billion parameters and 4 trillion training bytes, with potential reductions in inference flops by up to 50%.\n\nKey Contributions:\n1. **Dynamic Patching**: BLT introduces a method for dynamically segmenting bytes into patches, which allows for efficient computation allocation based on the entropy of the next byte. This approach eliminates the need for a fixed vocabulary, unlike traditional tokenization methods.\n \n2. **Scaling Study**: The paper presents the first flop-controlled scaling study of byte-level models, showing that BLT can be scaled effectively without a fixed vocabulary. The study finds that BLT models achieve better scaling trends than tokenization-based architectures, particularly when simultaneously increasing both patch and model size.\n\n3. **Efficiency and Robustness**: BLT improves both training and inference efficiency by selecting longer patches when data is predictable. It also enhances robustness to noisy inputs and improves character-level understanding, which is demonstrated on tasks involving orthographic knowledge, phonology, and low-resource machine translation.\n\n4. **Architectural Innovations**: BLT's architecture includes a large global latent transformer and smaller local models for encoding and decoding byte sequences. The use of cross-attention layers and hash n-gram embeddings contributes to its efficiency and performance.\n\n5. **Robustness to Noise**: The paper highlights BLT's robustness to input noise and its ability to handle character-level tasks better than tokenization-based models, which struggle with byte-level noise and character manipulation.\n\n6. **Multilingual and Low-Resource Translation**: BLT shows improved performance in translating between languages, particularly in low-resource settings, indicating its ability to generalize across diverse linguistic contexts.\n\n7. **Byte-ifying Tokenizer-Based Models**: The study explores initializing BLT from pre-trained tokenizer-based models like Llama 3, showing potential for reducing training flops while maintaining or improving performance.\n\nOverall, BLT represents a significant shift in LLM architecture by removing the dependency on tokenization, offering a scalable and robust framework for more efficient language models. The paper suggests that BLT could be a promising alternative to traditional approaches, especially in scenarios where inference efficiency and robustness are critical. Future work could explore further optimization of scaling laws specific to BLT and additional architectural refinements to enhance performance at larger scales.","notes":{"id":18,"name":"notes","body":null,"record_type":"Paper","record_id":19,"created_at":"2024-12-24T01:48:05.172Z","updated_at":"2024-12-24T01:48:05.172Z"},"created_at":"2024-12-24T01:47:49.045Z","updated_at":"2024-12-24T01:48:10.941Z"}