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Byte Pair Encoding: How a 90s Compression Trick Became the Secret Sauce of LLMs
- Byte Pair Encoding (BPE) was introduced in 1994 as a data compression technique by Philip Gage.
- Initially used for compression, BPE's adaptive nature later found applications in NLP, particularly in tokenization.
- In NLP, BPE tokenizes text into subword units by iteratively merging the most frequent pair of adjacent symbols.
- BPE balances between word-level and character-level tokenization, preserving word fragments while decomposing rare words.
- The method involves merging frequent symbol pairs to create a vocabulary of subword units reflecting language patterns.
- Efficient for language models, BPE optimizes sequences and balances computational load, memory requirements, and generalization.
- BPE's utility lies in its ability to handle diverse vocabularies, making it suitable for various languages and domains.
- BPE's success is attributed to its pragmatic approach of optimizing trade-offs, rather than just focusing on a single objective.
- The merge table in BPE stores symbol pairs for consistent tokenization of new words based on training data patterns.
- BPE exemplifies how simple, repurposed algorithms can form the foundation of advanced AI models like GPT-3 and GPT-4.
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