Tokenization Strategies Comparison for LLMs

What is Tokenization

Tokenization is the process of splitting text into tokens that can be processed by the model. Different tokenization strategies directly affect model efficiency and effectiveness.

Mainstream Algorithms Comparison

BPE (Byte Pair Encoding)

BPE builds vocabulary by iteratively merging the most frequent character pairs:

BPE pseudocode

while len(vocab) < max_vocab_size:
    find_most_frequent_pair()
    merge_pair()

Advantages:
- Controllable vocabulary size
- Can handle out-of-vocabulary words

Disadvantages:
- Slow training speed
- May produce unreasonable splits

WordPiece

WordPiece is the tokenization method used by BERT, similar to BPE but with different merging criteria.

Unigram

Unigram uses a probabilistic model, starting with a large vocabulary and progressively pruning.

Selection Recommendations

- General scenarios: BPE
- Chinese processing: Character-level + BPE
- Multilingual: SentencePiece

Summary

Choosing the right tokenization strategy requires balancing vocabulary size, training efficiency, and model effectiveness.