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The paper challenges the assumption that language models need explicit tokenizers to be efficient demonstrating that standard flat Transformers can process raw byte sequences and actually outperform traditional subword models as parameter sizes scale. The prevailing thought in the field has been that processing raw bytes is computationally inefficient because the sequences are substantially longer. A typical subword contains around four bytes. The authors argue that this extra sequence length is actually a feature that provides useful additional computation under the same parameter budget. By
Assuming I have been on hibernation since 2018-19 time frame, which materials - books, MOOC courses and YT channels would be recommended for me to get started with understanding all the current progress on AI/ML. Ideally a resource like TeachYourselfCS that can give structure and roadmap to navigate all the progress/knowledge on the domain would really help. Do we have to take different approaches to understand LLMs vs other type of ML models which have been in large scale deployment/usage from pre-2022/21 time frame? Thanks a lot in advance.
