All You Need to Know About Tokenization in LLMs

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Tokenization is a foundational step in the development and training of large language models (LLMs). It bridges raw text and machine-readable data, enabling models to understand and generate human language. In this comprehensive guide, you'll learn what tokenization is, why it's essential, the evolution of tokenization methods, and how to build your own tokenizer using the Byte Pair Encoding (BPE) algorithm. Whether you're a researcher, developer, or AI enthusiast, this article will equip you with the knowledge to optimize tokenization for better model performance.

What Is Tokenization?

Tokenization is the process of breaking down text into smaller units—tokens—that can be processed by a language model. These tokens may represent words, subwords, or even individual characters. For example, the sentence "Hello, world!" can be split into four tokens: ["Hello", ",", "world", "!"].

In the context of LLMs, tokenization transforms unstructured text into structured numerical sequences. Each token is assigned a unique integer ID, which maps to a vector in an embedding table. This vector becomes the input representation for the model during training or inference.

Without tokenization, language models would be unable to interpret raw text. It's the critical first step that converts linguistic data into a format suitable for deep learning architectures like transformers.

Why Do We Need Tokenization?

Machine learning models operate on numbers—not text. When pre-training an LLM on 10 GB of textual data, the first challenge is converting that text into numerical form. That’s where tokenization comes in.

The typical pipeline looks like this:

  1. Tokenize the raw text into discrete units.
  2. Map each token to a unique integer.
  3. Embed these integers into dense vectors using a lookup table.
  4. Feed the vectors into the transformer model.

During inference, the reverse happens: model outputs are converted from integers back into readable text.

Tokenization ensures efficiency, scalability, and cross-lingual compatibility—making it indispensable in modern NLP workflows.

Naive Tokenization Approaches

Early approaches to tokenization include character-level and word-level schemes. While simple, they come with trade-offs.

Character-Level Tokenization

This method treats every character as a token. The vocabulary size is small (e.g., ~256 for ASCII), but sequences become very long. For instance, the word "language" becomes eight separate tokens.

Drawbacks:

Word-Level Tokenization

Here, each word is a token. While this reduces sequence length, it explodes vocabulary size—especially with inflections, spelling variations, and multilingual data.

Drawbacks:

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Why Unicode ord() Isn't Suitable

One might consider using Python’s ord() function, which returns the Unicode code point of any character. However, this leads to:

While universal in coverage, it’s impractical for efficient model training.

UTF-8 Encoding: A Better Starting Point

UTF-8 encodes characters into 1–4 bytes, offering backward compatibility with ASCII and broad language support. Each byte ranges from 0 to 255, limiting initial vocabulary to 256.

But using raw UTF-8 byte sequences results in long token lists. To overcome this, we combine UTF-8 with subword tokenization, specifically Byte Pair Encoding (BPE).

Byte Pair Encoding (BPE): The Gold Standard

BPE is a data compression technique adapted for subword tokenization. It iteratively merges the most frequent byte pairs into new tokens, expanding vocabulary while shortening sequences.

How BPE Works

Given the string:
aaabdaaabac

  1. Count all adjacent byte pairs → "aa" appears most frequently.
  2. Replace "aa" with a new token Z → ZabdZabac
  3. Repeat: "ab" → Y, resulting in ZYdZYac
  4. Continue merging until desired vocab size is reached.

Decoding reverses these steps using a merge table.

This dynamic approach balances vocabulary size and sequence length—ideal for transformer models.

Building Your Own Tokenizer in Python

Let’s walk through implementing BPE from scratch.

Step 1: Encode Text Using UTF-8

raw_bytes = text.encode("utf-8")
tokens = list(raw_bytes)  # Convert to list of integers (0–255)

Step 2: Count Byte Pair Frequencies

def get_stats(tokens):
    stats = {}
    for a, b in zip(tokens, tokens[1:]):
        stats[(a, b)] = stats.get((a, b), 0) + 1
    return stats

Step 3: Merge Most Frequent Pairs

def merge(tokens, pair, idx):
    result = []
    i = 0
    while i < len(tokens):
        if i < len(tokens) - 1 and (tokens[i], tokens[i+1]) == pair:
            result.append(idx)
            i += 2
        else:
            result.append(tokens[i])
            i += 1
    return result

Step 4: Train the Tokenizer

vocab_size_final = 300
num_merges = vocab_size_final - 256
merges = {}

for i in range(num_merges):
    stats = get_stats(tokens)
    if not stats:
        break
    top_pair = max(stats, key=stats.get)
    idx = 256 + i
    print(f"Merging {top_pair} → {idx}")
    tokens = merge(tokens, top_pair, idx)
    merges[top_pair] = idx

Step 5: Implement Encode & Decode Functions

# Build vocab
vocab = {i: bytes([i]) for i in range(256)}
for (p0, p1), idx in merges.items():
    vocab[idx] = vocab[p0] + vocab[p1]

# Decode: IDs → Text
def decode(ids):
    tokens = b"".join(vocab[i] for i in ids)
    return tokens.decode("utf-8", errors="replace")

# Encode: Text → IDs
def encode(text):
    ids = list(text.encode("utf-8"))
    while len(ids) >= 2:
        stats = get_stats(ids)
        pair = min(stats.keys(), key=lambda p: merges.get(p, float("inf")))
        if pair not in merges:
            break
        ids = merge(ids, pair, merges[pair])
    return ids

You now have a fully functional tokenizer!

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Frequently Asked Questions

Q: What is the main advantage of BPE over word-level tokenization?
A: BPE reduces vocabulary size while handling rare and out-of-vocabulary words effectively through subword units.

Q: Can BPE handle multiple languages?
A: Yes! Since BPE operates on UTF-8 bytes, it naturally supports any language included in the training corpus.

Q: How does tokenization affect model performance?
A: Efficient tokenization shortens input sequences, allowing transformers to capture longer contexts and improve accuracy.

Q: Is character-level tokenization ever useful?
A: Rarely. It's mostly used in specialized models or when working with highly morphological languages—but generally inefficient.

Q: Should I train my tokenizer on diverse datasets?
A: Absolutely. Training on multilingual and domain-rich data leads to shorter sequences and better cross-lingual generalization.

Q: How do real-world LLMs like GPT handle tokenization?
A: They use variants of BPE (e.g., OpenAI's tokenizer), trained on massive, diverse corpora to maximize coverage and efficiency.

Final Thoughts

Tokenization is far more than a preprocessing step—it's a strategic lever for improving LLM efficiency, speed, and multilingual capability. From naive character and word splits to intelligent subword methods like BPE, the evolution reflects deeper understanding of language structure and computational constraints.

By training your own tokenizer on UTF-8 byte sequences and applying BPE merging rules, you gain full control over vocabulary design and model input structure. This knowledge empowers you to build more efficient, scalable, and globally inclusive language models.

As AI advances, so too will tokenization techniques—but mastering BPE lays a solid foundation for future innovation.

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