Byte-Level Byte Pair Encoding (BBPE)

Byte-Level Byte Pair Encoding (BBPE) is a variation of the Byte Pair Encoding (BPE) algorithm that operates on sequences of bytes rather than sequences of characters. This approach offers advantages, particularly in multilingual settings and for handling out-of-vocabulary (OOV) words.

How BBPE Works

BBPE follows the core principles of BPE, but the fundamental unit of operation is a byte. The process involves:

1. Conversion to Byte Sequence: The input text is first converted into a sequence of bytes. Each character, regardless of its complexity or representation (e.g., ASCII, UTF-8), is represented by its corresponding byte(s).
2. Application of BPE: The standard BPE algorithm is then applied to this byte sequence. This involves iteratively merging the most frequent adjacent byte pairs to build a vocabulary of frequently occurring byte sequences.

Example 1: Simple ASCII Word

Let's consider the English word "best" as input.

• Character Sequence: b e s t
• Byte Sequence Conversion: Each character is converted to its ASCII byte representation:
  • b -> 62
  • e -> 65
  • s -> 73
  • t -> 74
• BBPE Input: 62 65 73 74

The BPE algorithm would then operate on this byte sequence, identifying frequent byte pairs to merge and build a vocabulary.

Example 2: Multilingual Character (Chinese)

Now, let's consider a Chinese word (e.g., "你好" which translates to "hello"). In UTF-8 encoding, this word is represented by multiple bytes per character.

• Character Sequence: 你 好
• Byte Sequence Conversion: In UTF-8, "你" is e4 bd a0 and "好" is e5 a5 bd.
  • 你 -> e4 bd a0
  • 好 -> e5 a5 bd
• BBPE Input: e4 bd a0 e5 a5 bd

BBPE would then analyze this byte sequence for frequent pairs.

Advantages of BBPE

BBPE offers several key benefits over character-level BPE:

• Multilingual Effectiveness: By operating at the byte level, BBPE naturally handles different character encodings and scripts without requiring language-specific preprocessing or large character sets. This makes it highly effective in multilingual environments.
• Handling OOV Words: BBPE can effectively represent and tokenize OOV words. Even if a word is not present in the training vocabulary, its constituent bytes (or common byte sequences) are likely to be present, allowing for a subword-level representation.
• Vocabulary Sharing: BBPE facilitates vocabulary sharing across multiple languages. Since bytes are universal, a common byte-level vocabulary can be built that is relevant to many languages, leading to more efficient model training and inference.
• Robustness to Noise and Variations: The byte-level representation can be more robust to minor character variations or noisy data compared to character-level methods.