What is a Token in AI & Why It’s Key to Building Better Models

Tokens are essential to how artificial intelligence (AI) works. The term refers to the individual information that AI models use to make predictions, so understanding how tokens work can help you get better results from these models. If you are working with a text-based AI model, a token might be a word, part of a word, or a group of characters. A model’s performance can vary greatly depending on how it processes and categorizes this data. Understanding how tokens work is crucial to build, optimize, and scale AI models effectively and efficiently. This article will provide a clear answer to the question, what is inference in machine learning and what is a token in AI? so you can confidently achieve your objectives.

AI inference APIs are valuable tools for achieving objectives, such as understanding how tokens work so you can build, optimize, and scale AI models more effectively and efficiently. These solutions provide flexible, efficient, and clear methods for navigating the complexities of AI inference so you can get back to what you do best, creating amazing AI applications.

What is a Token in AI and Its Key Characteristics

Tokens are the basic units of data that AI applications understand. Under the hood of every AI application are algorithms that churn through data in their own language, one based on a vocabulary of tokens. Tokens are tiny data units that come from breaking down bigger chunks of information.

Speed Matters

AI models process tokens to learn the relationships between them and unlock capabilities, including:

• Prediction
• Peneration
• Reasoning

The faster tokens can be processed, the faster models can learn and respond.

What Is Tokenization?

Whether a transformer AI model is processing text, images, audio clips, videos, or another modality, it will translate the data into tokens. This process is known as tokenization. Efficient tokenization helps reduce the amount of computing power required for:

• Training
• Inference

Token Breakdown

There are numerous tokenization methods, and tokenizers tailored for specific data types and use cases can require a smaller vocabulary. There are fewer tokens to process. For large language models (LLMs), short words may be represented with a single token, while longer words may be split into two or more tokens. The word darkness would be split into two tokens:

• Dark
• Ness

Each token bears a numerical representation, such as:

• 217
• 655

Similar Splits

The opposite word, brightness, would similarly be split into:

• Bright
• Ness

These tokens have corresponding numerical representations, such as:

• 491
• 655

Contextual Tokens

The shared numerical value associated with ness can help the AI model understand that the words may have something in common. In other situations, a tokenizer may assign different numerical representations for the same word depending on its meaning in context.

The word lie could refer to a resting position or saying something untruthful. During training, the model would learn the distinction between these two meanings and assign them different token numbers. A tokenizer can help map visual inputs like pixels or voxels into discrete tokens for visual AI models that process images, video, or sensor data.

Audio Tokens

Models that process audio may turn short clips into spectrograms, visual depictions of sound waves over time that can then be processed as images.

Other audio applications may instead focus on capturing the meaning of a sound clip containing speech, and use another tokenizer that captures semantic tokens, which represent language or context data instead of simply acoustic information. The tokenization process involves several steps:

• Splitting: Dividing text into smaller units (words, subwords, punctuation).
• Normalization: Standardizing text, like converting all characters to lowercase, to reduce complexity.
• Mapping: Assigning a unique numerical identifier to each token.
• Visualization: Tokenization Example
• Example Text: “AI is evolving rapidly.”
• Tokenized Version: [‘AI’, ‘is’, ‘evolving’, ‘rapid’, ‘ly’, ‘.’]

Technique 1: Word Tokenization

Description: Simple, straightforward, effective for basic NLP tasks (e.g., text classification, sentiment analysis).

Weaknesses: Struggles with compound words, abbreviations, and languages without clear word boundaries (e.g., Chinese).

Use Cases: Sentiment analysis, text classification, information retrieval.

Technique 2: Character Tokenization

Description: Effective for languages without clear word boundaries, handling typos, spelling variations, and special symbols.

Weaknesses: It produces long sequences that can be harder for models to process and may not effectively capture semantic meaning.

Use Cases: Spelling correction, language processing for non-standard texts, neural network models using character inputs.

Technique 3: Subword Tokenization

Description: Balances between word and character tokenization; handles out-of-vocabulary (OOV) words, retains meaning for complex terms.

Weaknesses: Can create too many subword units, leading to inefficiencies in some cases; requires more advanced algorithms.

Use Cases: Modern language models (e.g., BERT, GPT), applications involving morphologically rich languages, handling OOV issues.

Types of Tokens Used in LLMs

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

Text Tokens

Text tokens represent words or parts of words. They are the most common type. 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
• Context

Punctuation Tokens

Punctuation tokens include commas, periods, and exclamation points. They keep the structure and flow of text. In “Wow, that’s cool!”, punctuation tokens are “,” and “!”. The tokens become [“Wow,” " that’s, “cool,” “!”]. They help LLMs:

• Understand where to pause
• Add emphasis
• Mark sentence ends

Without punctuation tokens, AI-generated text would sound robotic.

Special 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.
• Special tokens manage text flow and formatting.

Combined Power

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
• Code completions

Related Reading

• Neural Network vs. Machine Learning

Why Are Tokens Important in AI?

When you type something into an AI model, like a chatbot, it doesn’t take the whole sentence and run with it. It chops it up into bite-sized pieces called tokens. These tokens can be:

• Whole words
• Parts of words
• Even single characters

Think of it as giving the AI smaller puzzle pieces to work with. It makes it much easier for the model to figure out what you’re trying to say and respond smartly. If you typed, "Chatbots are helpful," the AI would split it into three tokens:

• "Chatbots"
• "are"
• "helpful"

Focused Understanding

Breaking it down like this helps the AI focus on each part of your sentence, making sure it:

• Gets what you're saying
• Gives a spot-on response

Context Is King. Tokens Help AI Understand It.

Tokens truly shine when advanced models like transformers step in. These models don’t just look at tokens individually. They analyze how the tokens relate to one another. This lets AI grasp the basic meaning of words and the subtleties and nuances behind them. Imagine someone saying, "This is just perfect."

Are they thrilled, or is it a sarcastic remark about a not-so-perfect situation? Token relationships help AI understand these subtleties, enabling it to provide:

• Spot-on sentiment analysis
• Translations
• Conversational replies

Tokens Turn Language Into Data That AI Understands

Once the text is tokenized, each token is transformed into a numerical representation, also known as a vector, using something called embeddings. Since AI models only understand numbers (there is no room for raw text), this conversion lets them work with language in a way they can process.

These numerical representations capture the meaning of each token, helping the AI do things like:

• Spotting patterns
• Sorting through text
• Creating new content

AI Translator

Without tokenization, AI would struggle to make sense of the text you type. Tokens serve as the translator, converting language into a form AI can process, making all its impressive tasks possible.

Tokens Help AI Manage Memory and Computation

Every AI model is limited by how many tokens it can handle at once, which is called the context window. You can think of it as the AI’s attention span, just like we can only focus on a limited amount at a time. By understanding how tokens work within this window, developers can optimize how the AI processes information, ensuring it stays sharp.

If the input text becomes too long or complex, the model prioritizes the most important tokens, ensuring it can deliver quick and accurate responses. This helps keep the AI running smoothly, even when dealing with large amounts of data.

Optimizing AI Models with Token Granularity

One of the best things about tokens is how flexible they are. Developers can adjust the size of the tokens to fit different types of text, giving them more control over how the AI handles language. Using word-level tokens is perfect for tasks like translation or summarization, while breaking down text into smaller subwords helps the AI understand rare or newly coined words.

This adaptability allows AI models to be fine-tuned for all applications, making them more accurate and efficient.

Boosting Flexibility With Tokenized Structures

By breaking text into smaller, bite-sized chunks, AI can more easily navigate different:

• Languages
• Writing styles
• Brand-new words

This is especially helpful for multilingual models, as tokenization helps the AI juggle multiple languages without confusion. Tokenization lets the AI take on unfamiliar words with ease. If it encounters a new term, it can break it down into smaller parts, allowing the model to make sense of it and adapt quickly.

Whether tackling a tricky phrase or learning something new, tokenization helps AI stay:

• Sharp
• On track

Making AI Faster and Smarter

Tokens are more than just building blocks. How they're processed can make all the difference in how quickly and accurately AI responds. Tokenization breaks down language into digestible pieces, making it easier for AI to:

• Understand your input
• Generate the perfect response

Whether it's conversation or storytelling, efficient tokenization helps AI stay:

• Quick
• Clever

Cost-Effective AI

Tokens are a big part of how AI stays cost-effective. The number of tokens the model processes affects the price; more tokens lead to higher costs.

By using fewer tokens, you can get faster and more affordable results, but using too many can lead to slower processing and a higher price tag. Developers should be mindful of token use to get great results without blowing their budget.

Related Reading

• What is Quantization in Machine Learning
• Batch Learning vs. Online Learning
• Feature Scaling in Machine Learning

How Are Tokens Used During AI Training and Inference?

Training an AI model starts with tokenizing the training dataset. The more complex the model, the larger the training dataset, leading to more tokens. For large language models, this number can be in the:

• Billions
• Trillions

Per the pretraining scaling law, the more tokens used for training, the better the quality of the AI model.

Model Learning

As an AI model is pretrained, it’s tested by being shown a sample set of tokens and asked to predict the next token. Whether or not its prediction is correct, the model updates itself to improve its next guess. This process is repeated until the model learns from its mistakes and reaches a target level of accuracy, known as model convergence.

After pretraining, models are improved by post-training, where they continue to learn on a subset of tokens relevant to the use case where they’ll be deployed. These could be tokens with domain-specific information for an application in law, medicine, or business, or tokens that help tailor the model to a specific task, like:

• Reasoning
• Chat
• Translation

Accurate Inference

The goal is a model that generates the right tokens to deliver a correct response based on a user’s query, a skill better known as inference.

Input and Output Tokens: The Basics of AI Inference

How are tokens used during AI inference and reasoning? During inference, an AI receives a prompt, which, depending on the model, translates into a series of tokens. The token may be:

• Text
• Image
• Audio clip
• Video
• Sensor data
• Gene sequence

The model:

• Processes these input tokens
• Generates its response as tokens
• Translates it to the user’s expected format

Context Window

Input and output languages can differ, such as in a model that translates English to Japanese or converts text prompts into images. To understand a complete prompt, AI models must be able to process multiple tokens at once. Many models have a specified limit, referred to as a context window, and different use cases require different context window sizes.

A model that can process a few thousand tokens at once can process a single high-resolution image or a few pages of text. With a context length of tens of thousands of tokens, another model can summarize a whole novel or an hour-long podcast episode.

Massive Input

Some models even provide context lengths of a million or more tokens, allowing users to input massive data sources for the AI to analyze.

Reasoning and AI Tokens: The Next Level of Inference

Reasoning AI models, the latest advancement in LLMs, can tackle more complex queries by treating tokens differently than before. The model also generates reasoning tokens over minutes or hours as it thinks about solving a given problem.

These reasoning tokens allow for better responses to complex questions, just like how a person can formulate a better answer given time to work through a problem. The corresponding increase in tokens per prompt can require over 100x more compute compared with a single inference pass on a traditional LLM, an example of test-time scaling, also known as long thinking.

Related Reading

• LLM Embeddings
• Domain Adaptation

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