(LLM Scaling-Meta) Byte Latent Transformer: Patches Scale Better Than Tokens
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(LLM Scaling-Meta) Byte Latent Transformer: Patches Scale Better Than Tokens
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Tune in to explore the Byte Latent Transformer (BLT), a groundbreaking new architecture from FAIR at Meta. Unlike traditional large language models that rely on fixed vocabularies and tokenizers, BLT is tokenizer-free, learning directly from raw bytes. Its novelty lies in dynamically grouping bytes into patches based on data complexity, allowing it to allocate compute efficiently.
BLT matches or surpasses the performance of state-of-the-art tokenization-based models like Llama 3 at scale, while offering significant improvements in inference efficiency, potentially using up to 50% fewer flops. It also provides enhanced robustness to noisy inputs and superior character-level understanding, excelling in tasks like orthography, phonology, and low-resource machine translation. Furthermore, BLT introduces a new scaling dimension, enabling simultaneous increases in model and patch size while maintaining a fixed inference budget.
Current limitations include the need for further research on BLT-specific scaling laws and potentially improving wall-clock efficiency. Join us to learn how this dynamic, byte-level approach could shape the future of language models!
Find the paper here: https://arxiv.org/pdf/2412.09871