What is a token in AI? Understanding how AI processes language with tokenization

What is a Token in AI?What is a Token in AI?

Large language models (LLMs) are designed to process text data as input and output additional text sequences as a response. These models process the text as smaller chunks called tokens. The tokens are embedded in an n-dimensional space that captures their features, such as semantics and context.

The tokenization process has been essential to developing these new-generation intelligence models. Modern architectures like transformers easily process these and produce excellent results. This article will discuss AI tokens in detail. We will understand how a token is defined and its role in LLMs.

What is a token?What is a token?

A token is the most fundamental data unit in a text document, essential for enabling AI to understand and process information. In Natural Language Processing (NLP), tokenization refers to breaking down larger texts into smaller, manageable pieces called tokens. Depending on the method, these tokens can be words, parts of words, or phrases. Before an AI model processes any input, it divides the text into these units, making it easier to analyze and generate responses.

However, tokens are not always cut precisely at word boundaries; they can include trailing spaces or sub-words. For example, “unbreakable” might be split into “un-” and “breakable.” This flexibility helps AI models handle various language structures. Understanding token length can be tricky, but there are some helpful rules of thumb:

• 1 token ≈ 4 characters in English
• 1 token ≈ ¾ words
• 100 tokens ≈ 75 words

To put this into perspective, Wayne Gretzky’s famous quote, “You miss 100% of the shots you don’t take,” contains 11 tokens. In contrast, the US Declaration of Independence transcript has around 1,695 tokens.

Tokenization is also language-dependent, meaning tokens in English might differ from those in other languages. For instance, “Cómo estás” (Spanish for “How are you”) translates into 5 tokens, even though it contains only 10 characters. This variability can affect the cost and complexity of processing text in different languages. OpenAI models, including GPT-3.5 and GPT-4, use specialized tokenizers to handle these differences, making tokenization a flexible and efficient process.

NLP behind the scenes in AINLP behind the scenes in AI

Natural Language Processing (NLP) enables AI to process and interpret human language, making digital interactions feel more intuitive. This starts with tokenizing — breaking down sentences into smaller units called tokens. Each token is analyzed individually, giving AI a structured framework to understand language.

After tokenization, these elements are transformed into numeric vectors, or embeddings, since AI models work best with numbers. These vectors capture word meanings, allowing AI to detect patterns, relationships, and context in text.

Initially, traditional techniques like Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW) were used to analyze text by focusing on word frequency, though they lacked depth in understanding context. Modern advancements in word embeddings, such as Word2Vec, GloVe, and BERT, have revolutionized AI’s comprehension by embedding words into compact, semantic-rich formats. Words with similar meanings are represented by similar vectors and are found closer in the latent space. This helps AI capture different contexts, language structures and nuanced relationships.

For example, BERT achieves an F1 score of 88.63% in sentiment analysis, outperforming older models like Word2Vec and GloVe. By processing context accurately, BERT can understand emotions, improve translations, and enhance information retrieval.

Here’s a comparison of popular NLP techniques:

Common tokenization techniquesCommon tokenization techniques

Here are the most common tokenization techniques:

Common tokenization techniquesCommon tokenization techniques

Word tokenization splits text into individual words. It’s a straightforward method that makes it easy to process simple language tasks. For instance, the sentence “Natural Language Processing is exciting” would become [“Natural,” “Language,” “Processing,” “is,” “exciting”].

This technique works well for tasks like sentiment analysis, text classification, and information retrieval. However, it can struggle with compound words, abbreviations, and languages without clear word boundaries, like Chinese.

Character tokenizationCharacter tokenization

Character tokenization breaks text into individual characters, capturing fine-grained details. For example, “Natural Language Processing” would be tokenized as [“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”].

This technique is effective for spelling correction, password analysis, and non-standard text input processing. It’s also useful for languages without clear word boundaries and models that learn from character-level inputs.

Subword tokenizationSubword tokenization

Subword tokenization splits words into smaller, meaningful units, such as prefixes, suffixes, or syllables. For “unbreakable,” this might be [“un,” “break,” “able”]. This method helps models handle out-of-vocabulary (OOV) words by recognizing parts of them, even if the full word is unfamiliar. Subword tokenization is popular in modern language models like BERT and GPT. It’s particularly useful for morphologically rich languages or when training on a limited vocabulary.

Types of tokens used in LLMsTypes of tokens used in LLMs

Large Language Models (LLMs) use different types of tokens to understand text. Here’s how each type differs:

Text tokensText tokens

Text tokens represent words or parts of words. They are the most common type. For example, the sentence “AI is fun” becomes [“AI,” “is,” “fun”]. Sometimes, they break down further into parts like [“A,” “I,” “is,” “fun”]. Text tokens help LLMs understand the main idea. They teach the model language patterns, grammar, and context.

Punctuation tokensPunctuation tokens

Punctuation tokens include commas, periods, and exclamation points. They keep the structure and flow of text. For example, in “Wow, that’s cool!”, punctuation tokens are “,” and “!”. So, the tokens become [“Wow,” " that’s, “cool,” “!”]. They help LLMs understand where to pause, add emphasis, or mark sentence ends. Without punctuation tokens, AI-generated text would sound robotic.

Special tokensSpecial tokens

Special tokens manage the text. They control how the model behaves. Common examples include:

• End of Text (<|endoftext|>): It signals the model to stop functioning like a period (.) that marks the end of a sentence.
• New Line (\n): Represents line breaks. Think of it as pressing 'Enter' on your keyboard.
• Padding Tokens (<pad>): They fill up space. Useful when working with batches of inputs.
• Special Instructions (<|sep|>): They separate parts of the prompt. Handy for dialogues or tasks.

LLMs use a mix of these token types to understand and process inputs effectively. Text tokens provide the core content, punctuation tokens help convey meaning accurately, and special tokens manage text flow and formatting. Using these types together helps LLMs generate coherent, context-aware, and grammatically correct responses. It allows LLM models to handle various tasks, from simple text generation to more complex dialogues and code completions.

LLM token limitsLLM token limits

Every LLM is limited in the number of tokens it can process at once. These limits (context windows) are key because they impact performance, cost, and efficiency.

Token limits define how much context an LLM can handle. Context includes prompts, instructions, and past exchanges. Higher token limits mean the model can manage longer inputs and keep context over extended conversations.

This leads to relevant and more accurate responses, especially for tasks with long texts or multi-turn dialogues. Models with higher token limits can deliver more detailed and nuanced outputs.

That said, increasing context window size doesn’t always lead to better performance. A study published on ResearchGate found that while larger token limits enhance understanding in certain scenarios, models with smaller token limits can still outperform models with larger context windows. For instance, retrieval-augmented models like LLaMA2-70B are better at summarization and responding to questions compared to GPT-3.5-turbo-16k, which has a larger token limit.

Here’s how the most popular LLM models set token limits:

Tokenization challengesTokenization challenges

Language is a complex medium and presents various challenges to tokenization. Here’s a more in-depth look at these challenges:

AmbiguityAmbiguity

Language is full of ambiguity. Words can have different meanings depending on context; tokenization alone can’t always resolve this. For instance, in the phrase “The chicken is ready to eat,” the word “eat” could be a verb or part of a noun phrase. Tokenization might misinterpret the intent without understanding the context. This leads to inaccurate analysis in tasks like sentiment analysis or translation.

• Lexical Ambiguity: Words like “bank” can mean a financial institution or a riverbank. Tokenizers may struggle to pick the correct meaning without more context.
• Compounding Issues: Compound words like “hot dog” (the food) versus “hot” and “dog” (temperature + animal) show how splitting or joining words can change the entire meaning.
• Named Entity Ambiguity: Words like “Apple” could refer to the fruit or the tech company. Tokenizers must decide when to treat these as generic terms or specific entities.

Language boundariesLanguage boundaries

Some languages, like Chinese, Japanese, and Thai, don’t use spaces between words. This makes tokenization difficult since traditional methods use spaces to identify word boundaries. For example, the Chinese phrase “苹果电脑” means “Apple computer,” but there are no gaps to indicate separate words.

A simple tokenizer might not know whether to treat it as “apple” and “computer” or as the brand name “Apple Computer.” Improper tokenization in these cases can lead to errors in machine translation, search engines, and even voice recognition.

• Word Segmentation: Tokenizers need advanced techniques, such as machine learning or dictionaries, to segment text properly in languages with continuous scripts.
• Compound Words: In German, words often combine into long compounds, like “Donaudampfschifffahrtsgesellschaftskapitän,” which means “Danube steamship company captain.” Tokenizers must understand whether to split them or keep them as one token.

Edge casesEdge cases

Edge cases involve special characters, numbers, abbreviations, or mixed formats that don’t fit standard tokenization rules. These require tokenizers to handle unique situations correctly. For example, “(452) 555-1212” is a phone number, but tokenizers might break it into [“(, ‘452’, ‘),’ ‘555’, ‘-,’ ‘1212’], losing the structure needed for accurate processing.

• Numbers and Symbols: Deciding whether to treat a phone number as one token or split it depends on the context, like dialing versus analyzing patterns.
• Email Addresses and URLs: An email like “user@example.com” or a URL like “https://www.example.com” is a cohesive unit, but tokenizers might break them down, leading to incorrect processing.
• Hyphenated Words and Acronyms: Words like “self-esteem” or “U.S.A.” could be treated as single tokens or split into parts. The right choice depends on the context.
• Programming Symbols: Understanding where variables and operators start and end is crucial in coding. Misinterpreting punctuation can lead to parsing errors.

ConclusionConclusion

Tokens are an important part of how AI works. They let models break down, analyze, and understand information. However, using tokens wisely is key to getting the best results. Efficient prompt engineering — keeping inputs clear and concise — helps conserve tokens and improve output quality. You can also request specific formats, like bullet points or tables, to get cleaner, more efficient responses.

Understanding tokenization will become even more important across different industries as AI evolves. AI is currently going beyond powering chatbots and processing data. People increasingly use this tool to streamline everyday tasks, enhance communication, and manage data effectively. Knowing how to optimize token usage ensures that AI remains a practical, powerful tool for your business.