Tokenization and Its Impact on AI Models

Tokenization is the process of converting raw text into discrete units—tokens—that AI models can process. Serving as the foundation for natural language understanding and generation, tokenization impacts everything from computational efficiency to semantic accuracy in large language models (LLMs).

In this guide, we cover:

Introduction to tokenization
Key tokenization strategies
Role of tokenization in AI model performance
Tokenization in modern architectures (GPT, DALL·E)
Challenges and best practices

The image shows an agenda with three points: "Tokenization – Introduction," "Types of Tokenization," and "Role of Tokenization in AI models."

What Is Tokenization?

Tokenization breaks raw text into tokens—words, subwords, characters, or punctuation—enabling AI models to convert language into numerical embeddings. Since LLMs cannot directly interpret raw text, tokenization standardizes inputs, making them suitable for tasks like generation, classification, or translation.

The image is a diagram explaining tokenization, showing the process of breaking down raw text into smaller units like words, sub-words, characters, and punctuations for AI processing.

The image explains the importance of tokenization, highlighting that models cannot process raw text and that tokenization standardizes input representation as vectors and embeddings.

Types of Tokenization

Each tokenization strategy balances vocabulary size, sequence length, and model performance. Here is a comparison of the main approaches:

Tokenization Type	Description	Advantages	Disadvantages
Word-Level	Splits text into full words.	Simple; preserves full word semantics.	Large vocabulary; cannot handle OOV words
Subword-Level (BPE)	Merges frequent character/subword pairs (Byte Pair Encoding).	Reduces OOV issues; smaller vocabulary.	More complex; may split meaningful units
Character-Level	Treats every character as a token.	No OOV issues; fine-grained control.	Very long sequences; low semantic density

1. Word-Level Tokenization

Word-level tokenization treats each word as an individual token. For example, “the cat wore a hat” becomes: the, cat, wore, a, hat.

The image illustrates types of tokenization, focusing on word-level tokenization where each word in the sentence "The cat wore a hat." is treated as a single token.

Advantages

Simple implementation
Preserves whole-word meaning

Disadvantages

Large vocabulary required
Cannot handle new or rare words (OOV)

The image is a comparison chart of the advantages and disadvantages of word-level tokenization. Advantages include being simple and intuitive, while disadvantages involve vocabulary size and handling out-of-vocabulary words.

2. Subword-Level Tokenization

Subword techniques like Byte Pair Encoding (BPE) iteratively merge the most frequent character or subword pairs, creating a flexible vocabulary.

The image explains sub-word-level tokenization, highlighting three steps: splitting words into smaller parts, using Byte Pair Encoding (BPE), and merging frequent character pairs or sub-words.

For example, “unhappiness” may become: un + happiness, and “don’t waste food” could split into: don + 't, waste, food.

The image illustrates types of tokenization, focusing on sub-word level, where the word "unhappiness" is split into "un" and "happiness."

Advantages

Handles rare and compound words
Balances vocabulary size and coverage

Disadvantages

More complex to implement
Split tokens can reduce readability

The image is a comparison chart of the advantages and disadvantages of sub-word-level tokenization. Advantages include handling rare words and reducing vocabulary size, while disadvantages include increased complexity and computational power requirements.

Note

Subword tokenization (BPE, WordPiece) is the most common approach in state-of-the-art language models to mitigate OOV issues and control vocabulary size.

3. Character-Level Tokenization

This method breaks text into individual characters, including spaces and punctuation. E.g., “It is raining.” becomes I, t, , i, s, , r, a, i, n, i, n, g, ..

The image explains types of tokenization, focusing on character-level tokenization where each character in the word "cat" is treated as a separate token.

The image illustrates character-level tokenization of the input text "It is raining." by breaking it down into individual characters.

Advantages

Resolves all OOV issues
Fine-grained text analysis

Disadvantages

Very long sequences increase compute
Low semantic density per token

The image presents a comparison of the advantages and disadvantages of character-level tokenization, highlighting benefits like no out-of-vocabulary issues and drawbacks such as increased computational complexity.

Role of Tokenization in AI Models

Effective tokenization influences:

Vocabulary & Memory
A larger vocabulary demands more memory and compute.
Handling OOV Words
Subword methods mitigate unseen-word errors.
Contextual Clarity
Proper token boundaries improve self-attention and disambiguation (e.g., “bank”).
Training Efficiency
Optimized tokenization reduces sequence length and speeds up convergence.

The image outlines the role of tokenization in AI models, highlighting its impact on vocabulary, efficiency, computational requirements, and model performance.

Tokenization in Modern AI Models

GPT Series (e.g., GPT-4)
Employs BPE-based subword tokenization to manage vocabulary size while handling rare or novel terms for coherent text generation.

DALL·E
Applies tokenization to both textual prompts and image patches, enabling cross-modal learning for text-to-image synthesis.

The image compares tokenization in GPT and DALL-E models, highlighting GPT's use of Byte Pair Encoding for text and DALL-E's tokenization for text prompts and image encoding.

Challenges and Considerations

Tokenization Bias
Frequency imbalances in training data can introduce model bias.
Language-Specific Tokenization
Scripts without spaces (Mandarin, Japanese) require specialized tokenizers.
Multilingual Models
Must efficiently handle diverse scripts and vocabularies for cross-lingual tasks.

Warning

Inadequate tokenization may amplify bias or degrade performance in specialized or multilingual scenarios. Always evaluate your tokenizer on representative data.

The image outlines challenges and considerations in tokenization, highlighting issues like tokenization bias, language-specific challenges, and impacts on multilingual models. It mentions specific languages such as Mandarin, Japanese, and Arabic, and the need for additional design considerations.

Conclusion

Tokenization underpins the success of NLP and multimodal AI systems. By selecting and fine-tuning the right tokenization strategy—whether word-level, subword-level, or character-level—you can significantly improve your model’s efficiency, accuracy, and fairness.

References

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