You have likely encountered the term “tokenization” when reading about large language models (LLMs), AI systems, or cybersecurity with AI. But do you know what tokenization is, how it has evolved, and why it is significant in AI?
As AI continues to improve in its ability to process human language and context, mastering tokenization is becoming crucial. For data experts, it is a game-changer, serving as a foundation for all the AI systems.
Let’s delve into the article to discover more about AI tokenization!
What Is Tokenization in AI?
Before diving into tokenization in AI, it is crucial to understand the concept of tokens.
AI tokens are the building blocks of AI systems that help language models, chatbots, and virtual assistants to generate and understand text. Each token is a smaller, secure unit representing words, sub-words, numbers, characters, or punctuations within sentences. Tokens are not always split up exactly where words begin or end; they might include trailing spaces or even parts of words.
According to OpenAI, one token typically includes four characters or ¾ words in English. Therefore, 100 tokens roughly equate to 75 words, though this can vary depending on the language and complexity of the text.
Beyond text, tokens also apply in other domains. In computer vision, a token could be an image segment, while in audio processing, it might be a sound snippet. This versatility allows AI to interpret and learn from different data formats.
Now that you clearly understand the meaning of AI tokens, let’s begin with AI tokenization.
Tokenization is the process of partitioning the text into tokens. Before tokenization, you will need to normalize the text to standardize it into a consistent format using NLP tools. After preprocessing, you can tokenize the text and add all the unique tokens to a vocabulary list with a numerical index.
Once tokenized, you must create embeddings, which are numerical vector representations of tokens. Each vector representation helps capture the token’s semantic meaning and relationships to other tokens.
Here is an example of tokenization and embeddings:
In the given illustration, you can see two special tokens:
- CLS is a classification token added at the beginning of the input sequence.
- SEP is a separator token that helps the model understand the boundaries of different segments of the input text.
The ultimate goal of tokenization is to create a vocabulary with tokens that makes the most sense to an AI model. To explore tokenization further, you can use OpenAI’s tokenizer tool.
Types of Tokenization Methods
Here are some commonly used tokenization methods:
- Space-Based Tokenization: This tokenization allows you to divide the text into words based on spaces. For instance, “I am Cool” would split into [“I”, “am”, “Cool”].
- Dictionary-Based Tokenization: In this tokenization, the text will be split into tokens according to a predefined dictionary. Words matching dictionary entries are treated as tokens. For example, “Llama is an AI model” can be tokenized as [“Llama”, “is”, “an”, “AI”, “model”] according to the dictionary.
- Byte-Pair Encoding Tokenization: This is a sub-word tokenization that helps segment the input text into tokens based on 2-byte or 3-byte units, which is common in models for languages like Chinese. Llama是一款AI工具 can be tokenized as [“Ll”, “ama”, “是”, “一”, “款”, “AI”，“工”，“具”].
How Can You Use the Tokenized and Embedded Data in AI Modeling?
To give AI tokens meaning, a deep learning or machine learning algorithm is trained on these tokenized and embedded data. After model training, AI systems learn to predict the next token in sequence or generate contextually relevant human-like text. Through iterative learning and fine-tuning, the performance of AI models can improve over time.
Evolution of AI Tokenization
In the early stages, tokenization was a fundamental way to break down text in linguistics and programming. As digital systems evolved, it became essential for securing sensitive data like social security numbers, credit card numbers, and other personal information. Tokenization helps to transform confidential data into a random token that is useless if stolen and can only be mapped back to the original details by an authorized entity.
With the advent of AI, tokenization has become even more critical, especially in NLP and machine learning tasks. Initially, tokenization in AI was a simple preprocessing task of splitting text into words, enabling early models to process and analyze language quickly. As AI models get smarter, the tokenization process helps you divide the text into subwords or even individual characters.
Such a tokenization approach allows LLMs like GPT-4 to capture the nuances and complexities of language, enabling them to understand and generate better responses. This evolution makes AI models more accurate in predictions, translations, summaries, and text creation across several applications, from chatbots to automated content creation.
Importance of Token in AI
In the previous section, you explored what a token AI is. Let’s discuss the two key factors to better understand the importance of AI tokens.
Token limits are a constraint for all LLMs, with each model having a maximum number of tokens it can process in a single input. These limits range from a few thousand tokens for smaller models to tens of thousands for larger commercial ones. Exceeding this limit can cause errors, confusion, and poor-quality responses from the AI. Token limits are essential in AI to ensure the model can effectively process the information given.
Cost is another important factor, as companies like OpenAI, Anthropic, Microsoft, and Alphabet charge based on token usage, typically pricing per 1,000 tokens. Therefore, the more tokens you use, the higher the cost of generating responses.
To manage tokens effectively, keep prompts concise and focused on a single topic or question, break long conversations into short ones, and summarize large blocks of text. You can also use an AI tokenizer tool to count tokens and estimate costs. For complex requests, consider a step-by-step approach rather than trying to include everything in one query.
How To Streamline Data Integration for Modern AI Systems with Airbyte?
Modern LLMs or AI systems are powerful tools for various applications, but they require the correct contextual data to function effectively. This data is often scattered across different sources, such as CRM platforms, databases, and warehouses. Additionally, maintaining a consistent pipeline to keep the data up-to-date is crucial. You cannot accomplish this easily with a one-time script or Python coding.
Airbyte, a data integration and replication platform, would be an excellent option to streamline this process. It allows you to transfer data from multiple systems to a destination of your choice through its 350+ pre-built connectors.
As the next step, you may need to preprocess the data, which includes tokenization, embeddings, and chunkings. Airbyte’s integrated support for RAG-specific transformations like LangChain-powered chunkings and OpenAI-enabled embeddings can help simplify the data integration process in a single step.
Let’s take a look at a few other features of Airbyte:
- Modern Generative AI workflows: Airbyte helps you streamline AI workflows by loading unstructured data like user reviews, social media posts, and emails into vector databases such as Pinecone and Weaviate. This integration facilitates the efficient retrieval of relevant data and helps identify similar patterns or trends.
- Efficient Data Transformation: If your organization aims to transform raw data into actionable insights while maintaining accuracy and consistency across your analytics workflows, Airbyte’s integration with dbt is an excellent solution. This powerful combination allows you to apply custom transformations within your data pipelines.
- Developer-Friendly Pipelines: If you are working within a Jupyter notebook, you can extract data from various systems into your Python workflows using Airbyte’s PyAirbyte library. This open-source, developer-friendly library allows you to interact with Airbyte connectors programmatically.
- Data Synchronization: Airbyte’s CDC feature enables you to capture and replicate updates or new transactions occurring in your operational databases to your destination system. This ensures that your destination reflects the most current data, ensuring accurate and up-to-date reporting and analysis.
- Open-Source: If your organization wishes to deploy Airbyte locally using Docker, you can do it with its open-source version. This will allow you to use all of Airbyte’s built-in connectors and most important features, like schema propagation, for data integration and replication.
- Data Security: By leveraging TLS/HTTPS encryption, SSH tunneling, and role-based access controls, you can confidently integrate and synchronize data by ensuring a high level of data protection. Compliance with ISO 27001 and SOC 2 Type II regulatory standards further ensures a safe integration process.
Summing It Up
Tokenization has evolved from a simple text processing technique to a powerful tool in diverse fields such as cybersecurity and AI. It serves as a foundational process in enabling AI systems to understand and generate human-like text. By breaking down data into manageable tokens, AI can process information more effectively. As AI continues to advance, understanding and optimizing tokenization will remain essential for building more accurate and efficient AI applications.
FAQs
What is an example of tokenization?
Performing tokenization in a sentence like “Advancements in AI make your interactions with technology more intuitive.” results in separate segments as: [“Advancements”, “in”, “AI”, “make”, “your”, “interactions”, “with”, “technology”, “more”, “intuitive”, “.”]
What is an example of a token in AI?
If a sentence is “AI is evolving rapidly,” then tokens are “AI”, “is”, “evolving”, and “rapidly”.