Byte Pair Encoding (BPE): Subword Tokenization for NLP & LLMs

Master Byte Pair Encoding (BPE), a key subword tokenization algorithm in NLP and LLMs. Learn how BPE handles OOV words and powers models like GPT and RoBERTa. Step-by-step guide.

Byte Pair Encoding (BPE)

Byte Pair Encoding (BPE) is a powerful subword tokenization algorithm widely adopted in Natural Language Processing (NLP) tasks. It's a cornerstone for models like GPT and RoBERTa, offering a significant advantage over traditional word-level tokenization by more effectively managing out-of-vocabulary (OOV) words. This documentation outlines the BPE process step-by-step with a practical example.

How BPE Works: A Step-by-Step Example

Let's illustrate the BPE algorithm using a small sample dataset with word frequencies.

Step 1: Prepare the Dataset

We start with a set of words and their occurrences:

  • cost (2 times)
  • best (2 times)
  • menu (1 time)
  • men (1 time)
  • camel (1 time)

Step 2: Create Character Sequences

Each word is broken down into a sequence of individual characters. We also track the frequency of these character sequences based on the original word frequencies.

  • costc o s t (2 times)
  • bestb e s t (2 times)
  • menum e n u (1 time)
  • menm e n (1 time)
  • camelc a m e l (1 time)

Step 3: Define the Target Vocabulary Size

We set a desired vocabulary size. This target includes all initial characters and any new subwords created through merging. For this example, let's aim for a vocabulary of 14 tokens.

Step 4: Initialize the Vocabulary with Unique Characters

The initial vocabulary consists of all unique characters present in the dataset.

Initial Vocabulary:

{a, b, c, e, l, m, n, o, s, t, u}

At this stage, we have 11 unique characters in our vocabulary.

Step 5: Iteratively Merge Frequent Symbol Pairs

The core of BPE involves repeatedly merging the most frequent adjacent symbol pairs. A "symbol" can be an individual character or an already formed subword.

Iteration 1: Merge s and t

  • Observation: The pair s t appears together in cost (2 times) and best (2 times), totaling 4 occurrences.
  • Merge: s + tst
  • Vocabulary Update:
    {a, b, c, e, l, m, n, o, s, t, u, st}
    (Vocabulary size: 12)

Iteration 2: Merge m and e

  • Observation: The pair m e appears in menu (1 time), men (1 time), and camel (1 time), totaling 3 occurrences.
  • Merge: m + eme
  • Vocabulary Update:
    {a, b, c, e, l, m, n, o, s, t, u, st, me}
    (Vocabulary size: 13)

Iteration 3: Merge me and n

  • Observation: The pair me n (formed by the new subword me and the character n) appears in menu (1 time) and men (1 time), totaling 2 occurrences.
  • Merge: me + nmen
  • Vocabulary Update:
    {a, b, c, e, l, m, n, o, s, t, u, st, me, men}
    (Vocabulary size: 14)

The merging process stops here, as we have reached our target vocabulary size of 14 tokens.

Final BPE Vocabulary

The resulting vocabulary constructed through this BPE process is:

{a, b, c, e, l, m, n, o, s, t, u, st, me, men}

Summary of BPE Steps

  1. Extract Data: Collect words from the dataset along with their frequencies.
  2. Define Target: Set the desired vocabulary size.
  3. Characterize Words: Split words into sequences of individual characters.
  4. Initialize Vocabulary: Populate the vocabulary with all unique characters.
  5. Identify Frequent Pairs: Find the most frequent adjacent symbol (character or subword) pairs.
  6. Merge Pairs: Merge the most frequent pair to form a new subword token and add it to the vocabulary.
  7. Iterate: Repeat steps 5 and 6 until the target vocabulary size is reached.

Using the BPE Vocabulary for Tokenization

Once the BPE vocabulary is built, it's used to tokenize new input text. Each word is broken down into the largest possible subword units that are present in the learned vocabulary. This strategy significantly improves the model's ability to represent rare and compound words, thereby reducing the occurrence of unknown or OOV tokens.

For instance, if the vocabulary contains st and men, the word cost would be tokenized as c, o, st, and best as b, e, st. The word men would be tokenized as men.

Key Concepts and Benefits

  • Subword Tokenization: Breaks words into smaller units, capturing morphological information and handling variations.
  • Out-of-Vocabulary (OOV) Management: Reduces OOV rates by representing unknown words as combinations of known subwords.
  • Vocabulary Size Control: Allows for a configurable vocabulary size, balancing expressiveness with model complexity.
  • Data Compression: By identifying and merging frequent character sequences, BPE can also be seen as a form of data compression.

Interview Questions on Byte Pair Encoding (BPE)

  • What is Byte Pair Encoding (BPE) in NLP?
  • How does BPE help handle out-of-vocabulary (OOV) words?
  • Describe the step-by-step process of building a BPE vocabulary.
  • Why do we merge the most frequent symbol pairs in BPE?
  • How do you decide when to stop merging in the BPE algorithm?
  • What is the initial vocabulary in BPE before merging?
  • How does BPE tokenize a new input word after vocabulary construction?
  • What are the advantages of BPE over word-level tokenization?
  • How is BPE used in models like GPT and RoBERTa?
  • Can BPE handle compound words effectively? Explain how.