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Artificial Intelligence (AI) is increasingly present in our daily lives, from chatbots like ChatGPT to machine translation systems to voice assistants. But how do these technologies understand and generate human language?
One of the fundamental concepts that allows AIs to process text is tokenization. This technique breaks a sentence into smaller units, called tokens, which can be words, parts of words or even individual characters. Without tokenization, AI models would not be able to understand, analyze and generate text effectively.
Understanding tokenization is essential for anyone who wants to delve deeper into how AI works, whether they are developers, researchers or simply technology enthusiasts. In this article, we will explain in a simple way what tokenization is, how it works and why it is so important in the field of Artificial Intelligence.
What is Tokenization?
Tokenization is the process of breaking down a text into smaller units called tokens. These tokens can be:
Whole words (e.g. "the", "cat", "jump")
Parts of words (e.g. "fr", "ien", "dly" in "friendly")
Single characters (e.g. "h", "o", "m", "e")
Depending on the method used, tokenization can be more or less detailed. For example, in advanced AI models such as those based on neural networks, tokens that represent word fragments are often used, to better handle languages with many morphological variations.giche.
How does tokenization work?
The tokenization process occurs in several steps:
Identifying spaces and punctuation
In written texts, spaces between words help determine the boundaries of tokens. However, some languages such as Chinese or Japanese do not use spaces between words, making the process more complex.
Recognition of words or word fragments
AI models use predefined dictionaries or advanced algorithms to determine how to break down text.
Assigning a numeric ID to each token
Once tokens are identified, they are converted into numbers that the model can process. For example, the sentence "The cat jumps" might become [42, 156, 98] in an AI model (i.e. a numeric vector).
Using tokens in AI models
Language models, such as those based on Transformers, use these tokens to process text and generate coherent responses.
Tokenization and Weights in AI Models
After tokenization, AI models do not directly interpret words, but work with numerical representations of tokens. This is where weights come into play (we talked about them in the article Simple Guide to Artificial Intelligence), which determine the importance of each token in a given context.
Tokens are transformed into numeric vectors
After tokenization, each token is converted into a numerical representation called embedding. Embedding is a method of data representation that allows words, images or sounds to be encoded as numerical vectors (e.g. [42, 156, 98] seen above) in a multidimensional space.
Weights influence understanding of context
AI models, such as those based on Transformers, use weights to assign greater or lesser importance to certain tokens based on the context of the sentence.
For example, in the sentence “The cat is on the carpet”, the model can learn that “cat” and “carpet” have a stronger relationship than “The” and “carpet.” This happens by updating the weights in the neural network layers.
Weights are optimized during model training
Through the process of machine learning, the model continuously updates its weights (through a process called backpropagation) to improve its understanding of language and generate more consistent responses.
Types of Tokenization
There are several approaches to tokenization, each with advantages and disadvantages:
Word Tokenization: Splits text into whole words. Simple, but problematic for languages with a lot of morphological flexibility.
Subword tokenization: Divides words into smaller units, better handling rare or novel words. Common techniques are Byte Pair Encoding (BPE) and WordPiece.
Character tokenization: Each character is a token. Useful for languages without spaces between words, but inefficient for alphabetic languages.
Why is Tokenization Important?
Allows AI models to understand text
Without tokenization, models would not know how to parse written language.
Improves computational efficiency
Breaking text into smaller units helps to better handle unknown words and reduce model complexity.
Facilitates machine translation and speech recognition
Systems like Google Translate, ChatGPT, and voice assistants use tokenization to interpret and generate sentences.
In summary
Tokenization is a fundamental step in natural language processing (NLP) and AI models. Without it, AIs would not be able to understand and generate text effectively. However, tokenization alone is not enough: weights in AI models play a key role in interpreting context, improving language understanding and the quality of generated responses.
Whether it is chatbots, machine translators or voice assistants, every language-based interaction first goes through a tokenization process and is then processed using weights learned by the model. Understanding these mechanisms helps us better understand how AIs work and why they are so powerful in processing human language.
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