Breaking Language into Tokens: How Transformers Process Information?

Community Article Published May 3, 2025

Reading Level: Beginner-friendly explanation for readers curious about AI fundamentals. No prior machine learning knowledge required.

This article explains how transformer models process information by breaking down text into tokens. Readers will learn:

What tokenization is and why it matters
How transformers turn text into something they can understand
The meaning of key terms like "token," "vocabulary," and "transformer"
Why these choices affect how well AI models work across different languages

TL;DR Transformers are powerful AI models that can understand and generate text. But before they can process language, they first break it into smaller pieces called tokens-a process known as tokenization. This step is crucial: it helps the model understand meaning, but also shapes how well it handles different languages and tasks. In this article, you'll get a beginner-friendly tour of how tokenization works, why it matters, and what it means for the future of AI.

Pre-requisite term Glossary:

Tokenization: The process of breaking text into smaller units called tokens (such as words, subwords, or characters) so that computers can process and understand language more effectively. For example, the sentence "Transformers are amazing" might become ["Transformers", "are", "amazing"] or even smaller pieces depending on the tokenizer used. [See: Zilliz Glossary]
Token: A basic unit of text, like a word, part of a word, or even a character, that the model uses as a building block for understanding and generating language. [See: Hugging Face Tokenizers]
Vocabulary: The complete set of unique tokens a model knows about and can use. The size and design of the vocabulary affect how well the model can handle different words and languages. [See: Zilliz Glossary]
Transformer: A type of AI model that processes sequences of data (like text) using layers of self-attention, allowing it to understand relationships between words regardless of their position in a sentence. Transformers are the backbone of modern AI models like GPT and BERT. [See: DataCamp: How Transformers Work]

Why Tokenization Matters

With over a decade of experience in machine learning, I've observed how a seemingly mundane preprocessing step becomes the silent determinant of model capabilities. When you ask any large language model a question, a critical transformation occurs before any prediction: your text is broken into "tokens" – the fundamental units these models process. This tokenization step isn't merely technical; it's a mathematical constraint that profoundly limits what these systems can understand.

Recent studies of cross-lingual transfer learning have shown that tokenization alone can account for substantial performance variance across languages-sometimes leading to significant downstream performance degradation and increased training costs, especially when using English-centric tokenizers for multilingual models (Petrov et al., 2023). For example, training costs can increase by up to 68% due to inefficient tokenization, and vocabulary size requirements for multilingual models can be up to three times larger than for English alone. This finding fundamentally changes how we should approach multilingual model design.

Tokenization poses a fascinating practical paradox: improve token efficiency and you immediately reduce computational cost, but potentially introduce biases and compromise generalization. Throughout this article, I'll share insights into tokenization techniques that have been shown to improve multilingual benchmark scores while reducing training costs.

Understanding the Challenge for Transformers

Tokenization for transformer models involves several competing objectives that make it challenging to design the perfect system:

Vocabulary size limit: Transformers can only handle a fixed number of unique tokens (usually between 10,000-100,000). This seems large, but consider that English has hundreds of thousands of words, and each possible misspelling would need its own token in a word-based system.
Information density: Each token should carry meaningful information. The token for "the" conveys less information than the token for "quantum," and ideally, the model would use fewer tokens for common words and more for information-rich ones.
Sequence length constraint: Transformer models have a fixed "context window"-they can only consider a certain number of tokens at once (ranging from 2,048 tokens in older models to 128,000+ in the newest versions). If your tokens are inefficient, you waste this precious space.
Language fairness: If English text requires fewer tokens than Japanese for the same content, English users get more "bang for their buck" in terms of context window usage.

Let's illustrate these challenges with a concrete example. Imagine a simple transformer model that can accept only 1,000 tokens at once. If your tokenization scheme is character-based (each letter is a token), you could fit roughly 1,000 characters-about 200 English words or a single page of text. If your scheme is word-based, you might fit 1,000 words-about 4-5 pages of text. This 5× difference dramatically affects what the model can "see" at once.

The tokenization dilemma bears a striking resemblance to apartment hunting in Manhattan: you're balancing size constraints, efficiency, location, and affordability. And just as New Yorkers develop elaborate hacks to maximize tiny living spaces, tokenization engineers have created increasingly sophisticated methods to pack maximum meaning into limited token real estate.

One particularly intriguing insight that even experts sometimes miss: tokenization inevitably creates a form of lossy compression that the model must learn to compensate for. While we focus on the obvious case of rare words being broken into subword pieces, even common words carry subtle positional information that gets lost in tokenization. The word "bank" means something different in "river bank" versus "bank account," but its token representation is identical in both cases. Transformer models must implicitly learn to reconstruct these contextual distinctions despite tokenization smoothing them away.

Here's how we might mathematically express the goal of tokenization-finding a vocabulary $V$ that balances these competing needs:

$L(V) = \alpha \cdot \sum_{l \in \text{Languages}} w_l \cdot \text{AvgSeqLen}_l(V) + \beta \cdot |V| + \gamma \cdot \text{CrossLingualVariance}(V)$

Don't worry if this formula looks intimidating-it simply means we're trying to minimize average sequence length (shorter is better) across languages, manage vocabulary size, and reduce unfairness between languages, with $\alpha$ , $\beta$ , and $\gamma$ being weights for each goal.

Byte Pair Encoding: How Transformer Models Learn Their Vocabulary

The most common tokenization method for transformer models is Byte Pair Encoding (BPE), which was adapted for natural language processing by Sennrich et al. in 2016 (Sennrich et al., 2016). This method powers the vocabularies of models like GPT-3, GPT-4, and many others.

How BPE works in simple terms:

Start with a vocabulary of single characters (a, b, c, etc.)
Look through your training data and find the most common pair of adjacent characters
Merge this pair into a new token and add it to your vocabulary
Repeat steps 2-3 thousands of times until you reach your desired vocabulary size

For example, if "th" appears frequently in English text, BPE would create a new token "th" after the first iteration. Later, it might merge "th" and "e" to create "the" as a single token.

Here's a simplified version of how BPE works in code:

# A simplified example of the BPE algorithm
def basic_bpe_example(text, num_merges):
    # Start with character vocabulary
    vocab = set(text)  # All unique characters
    
    # Initial tokenization: each character is a separate token
    tokens = [[c] for c in text]  # Each character as separate token
    
    for i in range(num_merges):
        # Count all adjacent pairs
        pair_counts = {}
        for token_list in tokens:
            for j in range(len(token_list) - 1):
                pair = (token_list[j], token_list[j+1])
                pair_counts[pair] = pair_counts.get(pair, 0) + 1
        
        # Find most frequent pair
        if not pair_counts:
            break  # No more pairs to merge
        
        best_pair = max(pair_counts.items(), key=lambda x: x[1])[0]
        
        # Create new merged token and add to vocabulary
        new_token = best_pair[0] + best_pair[1]
        vocab.add(new_token)
        
        # Apply the merge throughout the text
        for i, token_list in enumerate(tokens):
            j = 0
            while j < len(token_list) - 1:
                if token_list[j] == best_pair[0] and token_list[j+1] == best_pair[1]:
                    token_list[j] = new_token  # Replace with merged token
                    token_list.pop(j+1)  # Remove the second token
                else:
                    j += 1
    
    return vocab, tokens

A lesser-known but fascinating insight: BPE carries an implicit "rich get richer" dynamic. Since it repeatedly merges the most frequent pairs, it tends to create more efficient tokens for already common patterns while leaving rare patterns inefficiently tokenized. This creates a compounding advantage for dominant languages and common usage patterns in the training data (Raschka, 2025).

Why Tokenization Matters: The Hidden Math Behind Transformer Understanding

To truly appreciate why tokenization is so crucial for transformer models, let's connect it to information theory-how computers efficiently encode and process information.

The Compression Connection: BPE is essentially implementing a form of data compression. When two characters or subwords frequently appear together, combining them into a single token saves space. This is similar to how ZIP files work-finding patterns to represent information more efficiently (Hugging Face LLM Course).

Tokenization is essentially the transformer's compression algorithm for dealing with the messy, redundant nature of human language-like a linguistic WinRAR trying to make sense of Shakespeare, tweets, code, and emoji all at once. And just like real file compression, there's always a tradeoff between file size and fidelity.

What makes this work for transformers specifically? Transformer models process tokens in parallel rather than sequentially. Each token gets its own position in the model's attention mechanism. By having meaningful subword tokens instead of just characters, transformers can establish relationships between meaningful units of language rather than just individual letters, vastly improving their understanding capabilities.

Visualizing Tokenization: How Different Models Split Your Text

To better understand how tokenization affects transformer models, let's look at a concrete example of how the same text gets tokenized by different schemes:

Original text: "Transformers revolutionized natural language processing"

Character-level tokenization: Each letter is a separate token

[T] [r] [a] [n] [s] [f] [o] [r] [m] [e] [r] [s] [ ] [r] [e] [v] [o] [l] [u] [t] [i] [o] [n] [i] [z] [e] [d] [ ] [n] [a] [t] [u] [r] [a] [l] [ ] [l] [a] [n] [g] [u] [a] [g] [e] [ ] [p] [r] [o] [c] [e] [s] [s] [i] [n] [g]

(54 tokens)

Word-level tokenization: Each word is a token

[Transformers] [revolutionized] [natural] [language] [processing]

(5 tokens)

BPE (like GPT-2/GPT-3): Subword tokens

[Trans][form][ers] [revolution][ized] [natural] [language] [process][ing]

(8 tokens)

Most production transformer models use BPE or similar subword approaches because they balance efficiency with flexibility (Brown et al., 2020).

Real-World Implications for Transformer Models

Understanding tokenization helps explain several "mysterious" behaviors you might observe when using transformer-based AI systems:

1. Why Transformers Struggle with Long Words

Have you ever noticed that language models sometimes make more mistakes with very long or unusual words? There's a mathematical reason for this. For example, the word "antidisestablishmentarianism" is broken down into several tokens, requiring the model to reassemble meaning across multiple pieces and increasing the chance for errors, especially if the model has seen few examples of these rare subword combinations during training.

2. Cross-Lingual Fairness Issues

Transformer models often perform better in English than in other languages, and tokenization is partly responsible. Consider this example:

English: "I love machine learning" (4 words)

GPT tokenization: ~4-5 tokens

Chinese: "我喜欢机器学习" (5 characters, same meaning)

GPT tokenization: ~10 tokens

Japanese: "私は機械学習が大好きです" (8 characters, same meaning)

GPT tokenization: ~12-15 tokens

This creates an inherent bias: English speakers can fit 2-3× more content in the same context window compared to Chinese or Japanese speakers. The technical term for this is Token Information Density (TID):

$\text{TID}(language, V) = \frac{\text{Information content}}{\text{Token count}}$

Studies show English has up to 2.5× higher TID than some other languages using standard BPE tokenization. This translates to:

More efficient context window usage for English

Higher quality outputs for the same input length

Lower costs for English users of commercial API-based models

Here's an expert insight that's rarely discussed: tokenization fairness doesn't just affect languages with different scripts. Even among Western European languages, the information density discrepancy can be significant. Languages with rich morphology (like Finnish) or compounding (like German) often experience lower token efficiency than English. The Finnish word "epäjärjestelmällistyttämättömyydelläänsäkäänköhän" (meaning approximately "I wonder if - even with their lack of capability to cause something to be unsystematic") would consume dozens of tokens while expressing a concept that might take just 15-20 tokens in English.

3. Token-Level Problems Visible in Outputs

If you've used transformer models extensively, you might have noticed some strange behaviors that directly trace back to tokenization:

Repetition loops: Models sometimes get stuck repeating phrases because of how tokenization creates feedback loops.
Hallucinations at token boundaries: Factual errors often occur at the boundaries between tokens.
"Thinking" mid-word: Models sometimes change direction mid-word because they're actually processing several tokens.
Inconsistent spacing: Unusual spacing patterns emerge because spaces are often attached to tokens rather than being separate.

The Future of Tokenization for Transformer Models

As transformer technology continues to evolve, three exciting directions in tokenization research promise to address current limitations:

Dynamic Context-Aware Tokenization: Adjusting how words are split based on the surrounding text, improving handling of technical vocabulary, named entities, and multilingual documents (Chen & Li, 2023).
Learning to Tokenize Better: Teaching models to create their own optimal vocabularies rather than using human-designed algorithms (Singh & Strouse, 2024).
Bridging Languages through Token Alignment: Aligning token representations across languages to improve performance and fairness for multilingual models (Remy et al., 2024).

After exploring the mathematics and practical implications of tokenization for transformer models, it's clear this seemingly technical detail profoundly shapes what these systems can understand and how they process language.

The key takeaways:

Tokenization creates an artificial language interface-Transformer models don't actually process human language; they process tokens. This artificial interface creates both capabilities and limitations.
Mathematical choices have real-world impacts-The decisions about how to create a vocabulary directly affect model performance, fairness across languages, and computational efficiency.
Observing tokenization helps explain model behavior-Many "mysterious" behaviors of language models make sense when you understand how their tokenization works.
The future is dynamic and learned-Next-generation transformer models will likely move beyond static vocabularies to more adaptive approaches that learn the optimal way to tokenize for different contexts.

As transformer models become increasingly central to our AI systems, understanding tokenization provides insight into their capabilities and limitations. It also reveals exciting possibilities for improvement-not just by making models bigger, but by fundamentally rethinking how they perceive and process human language.