Large Language Models (LLMs) often employ advanced tokenization techniques to effectively process and understand text. Tokenization is the process of splitting text into smaller units, called tokens. These tokens can be words, subwords, or even individual characters. The choice of tokenization affects the vocabulary size, the handling of out-of-vocabulary words, and the overall performance of the model. Here are some of the most common tokenization techniques used in LLMs:
Byte Pair Encoding (BPE):
- Originally used for data compression, BPE is a subword tokenization method where frequent pairs of characters (or byte pairs) are merged to form new tokens. This process is repeated iteratively and builds a vocabulary of subwords based on the frequency of their occurrences in the dataset.
WordPiece:
- WordPiece is similar to BPE but uses a different criterion for merging tokens. Instead of relying solely on frequency, WordPiece merges tokens based on a likelihood-based metric, optimizing for a reduction in the overall language modeling loss.
SentencePiece:
- SentencePiece is a tokenization library that treats the input as a raw text stream, thus allowing the model to learn subword units directly from raw text. SentencePiece implements both BPE and a unigram language model-based tokenization, making it robust to language variations and flexible in its application.
Unigram Language Model:
- This approach trains a unigram language model on the training corpus and iteratively prunes tokens based on their contribution to the likelihood of the corpus. Over time, it learns an optimal set of subword units that represents the text efficiently.
Character-Level Tokenization:
- In character-level tokenization, text is split into individual characters. This approach ensures there are no out-of-vocabulary tokens but can produce very long sequences, which may be inefficient for certain tasks.
Word-Level Tokenization:
- This technique splits text into words based on spaces and punctuation. It is straightforward but can lead to large vocabularies and deal poorly with out-of-vocabulary words, especially in languages with rich morphology or those that do not use spaces between words.
Morpheme-Based Tokenization:
- Some languages, particularly those with complex morphology, benefit from morpheme-based tokenization, where words are split into morphemes (the smallest semantic units in a language). This approach can be more information-rich but also more complex.
Hybrid Approaches:
- LLMs might use hybrid tokenization systems that combine different methods to balance the advantages of each. For example, they could use word-level tokenization for common words and subword tokenization for rare or complex words.
The choice of tokenization technique depends on the language, the task, and the specific requirements of the LLM. For instance, subword tokenization methods like BPE and WordPiece have become particularly popular in models like BERT, GPT, and their successors because they offer a good trade-off between vocabulary size and sequence length, effectively managing both common and rare words.