VentureBeat December 18, 2024
The AI research community continues to find new ways to improve large language models (LLMs), the latest being a new architecture introduced by scientists at Meta and the University of Washington.
Their technique, Byte latent transformer (BLT), could be the next important paradigm for making LLMs more versatile and scalable.
BLT solves one of the longstanding problems of LLMs that operate at byte level as opposed to tokens. BLT can open the way for new models that can process raw data, are robust to changes and don’t rely on fixed vocabularies.
Tokens vs bytes
Most LLMs are trained based on a static set of tokens, predefined groups of byte sequences.
During inference, a tokenizer breaks the input sequence down into...
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