Byte pair encoding[1][2] (also known as digram coding)[3] is an algorithm, first described in 1994 by Philip Gage for encoding strings of text into tabular form for use in downstream modeling.[4] Its modification is notable as the large language model tokenizer with an ability to combine both tokens that encode single characters (including single digits or single punctuation marks) and those that encode whole words (even the longest compound words).[5][6][7] This modification, in the first step, assumes all unique characters to be an initial set of 1-character long n-grams (i.e. initial "tokens"). Then, successively the most frequent pair of adjacent characters is merged into a new, 2-character long n-gram and all instances of the pair are replaced by this new token. This is repeated until a vocabulary of prescribed size is obtained. Note that new words can always be constructed from final vocabulary tokens and initial-set characters.[8]
All the unique tokens found in a corpus are listed in a token vocabulary, the size of which, in the case of GPT-3.5 and GPT-4, is 100256.
The difference between the modified and the original algorithm is that the original algorithm does not merge the most frequent pair of bytes of data, but replaces them by a new byte that was not contained in the initial dataset. A lookup table of the replacements is required to rebuild the initial dataset. The algorithm is effective for tokenization because it has low computational overhead and remains consistent and reliable.
The original algorithm operates by iteratively replacing the most common contiguous sequences of characters in a target text with unused 'placeholder' bytes. The iteration ends when no sequences can be found, leaving the target text effectively compressed. Decompression can be performed by reversing this process, querying known placeholder terms against their corresponding denoted sequence, using a lookup table. In the original paper, this lookup table is encoded and stored alongside the compressed text.
Exampleedit
Suppose the data to be encoded is
aaabdaaabac
The byte pair "aa" occurs most often, so it will be replaced by a byte that is not used in the data, such as "Z". Now there is the following data and replacement table:
ZabdZabac Z=aa
Then the process is repeated with byte pair "ab", replacing it with "Y":
ZYdZYac Y=ab Z=aa
The only literal byte pair left occurs only once, and the encoding might stop here. Alternatively, the process could continue with recursive byte pair encoding, replacing "ZY" with "X":
XdXac X=ZY Y=ab Z=aa
This data cannot be compressed further by byte pair encoding because there are no pairs of bytes that occur more than once.
To decompress the data, simply perform the replacements in the reverse order.
^Sennrich, Rico; Birch, Alexandra; Haddow, Barry (2015-08-31). "Neural Machine Translation of Rare Words with Subword Units". arXiv:1508.07909 [cs.CL].
^Paaß, Gerhard; Giesselbach, Sven (2022). "Pre-trained Language Models". Foundation Models for Natural Language Processing. Artificial Intelligence: Foundations, Theory, and Algorithms. pp. 19–78. doi:10.1007/978-3-031-23190-2_2. ISBN9783031231902. Retrieved 3 August 2023.
March 07, 2024
byte, pair, encoding, also, known, digram, coding, algorithm, first, described, 1994, philip, gage, encoding, strings, text, into, tabular, form, downstream, modeling, modification, notable, large, language, model, tokenizer, with, ability, combine, both, toke. Byte pair encoding 1 2 also known as digram coding 3 is an algorithm first described in 1994 by Philip Gage for encoding strings of text into tabular form for use in downstream modeling 4 Its modification is notable as the large language model tokenizer with an ability to combine both tokens that encode single characters including single digits or single punctuation marks and those that encode whole words even the longest compound words 5 6 7 This modification in the first step assumes all unique characters to be an initial set of 1 character long n grams i e initial tokens Then successively the most frequent pair of adjacent characters is merged into a new 2 character long n gram and all instances of the pair are replaced by this new token This is repeated until a vocabulary of prescribed size is obtained Note that new words can always be constructed from final vocabulary tokens and initial set characters 8 All the unique tokens found in a corpus are listed in a token vocabulary the size of which in the case of GPT 3 5 and GPT 4 is 100256 The difference between the modified and the original algorithm is that the original algorithm does not merge the most frequent pair of bytes of data but replaces them by a new byte that was not contained in the initial dataset A lookup table of the replacements is required to rebuild the initial dataset The algorithm is effective for tokenization because it has low computational overhead and remains consistent and reliable Contents 1 Original algorithm 1 1 Example 2 See also 3 ReferencesOriginal algorithm editThe original algorithm operates by iteratively replacing the most common contiguous sequences of characters in a target text with unused placeholder bytes The iteration ends when no sequences can be found leaving the target text effectively compressed Decompression can be performed by reversing this process querying known placeholder terms against their corresponding denoted sequence using a lookup table In the original paper this lookup table is encoded and stored alongside the compressed text Example edit Suppose the data to be encoded is aaabdaaabac The byte pair aa occurs most often so it will be replaced by a byte that is not used in the data such as Z Now there is the following data and replacement table ZabdZabac Z aa Then the process is repeated with byte pair ab replacing it with Y ZYdZYac Y ab Z aa The only literal byte pair left occurs only once and the encoding might stop here Alternatively the process could continue with recursive byte pair encoding replacing ZY with X XdXac X ZY Y ab Z aa This data cannot be compressed further by byte pair encoding because there are no pairs of bytes that occur more than once To decompress the data simply perform the replacements in the reverse order See also editRe Pair Sequitur algorithmReferences edit Gage Philip 1994 A New Algorithm for Data Compression The C User Journal A New Algorithm for Data Compression Dr Dobb s Journal 1 February 1994 Retrieved 10 August 2020 Witten Ian H Moffat Alistair Bell Timothy C 1994 Managing Gigabytes New York Van Nostrand Reinhold ISBN 978 0 442 01863 4 Byte Pair Encoding Archived from the original on 2016 03 26 Sennrich Rico Birch Alexandra Haddow Barry 2015 08 31 Neural Machine Translation of Rare Words with Subword Units arXiv 1508 07909 cs CL Brown Tom B Mann Benjamin Ryde r Nick Subbiah Melanie Kaplan Jared Dhariwal Prafulla Neelakantan Arvind Shyam Pranav Sastry Girish Askell Amanda Agarwal Sandhini 2020 06 04 Language Models are Few Shot Learners arXiv 2005 14165 cs CL google sentencepiece Google 2021 03 02 Retrieved 2021 03 02 Paass Gerhard Giesselbach Sven 2022 Pre trained Language Models Foundation Models for Natural Language Processing Artificial Intelligence Foundations Theory and Algorithms pp 19 78 doi 10 1007 978 3 031 23190 2 2 ISBN 9783031231902 Retrieved 3 August 2023 Retrieved from https en wikipedia org w index php title Byte pair encoding amp oldid 1211805790, wikipedia, wiki, book, books, library,
