In a world where natural language processing and generative AI are thriving, “tokens” is quite a common word if you dig into the details.
In this blog, we will learn all about tokens in AI, what they are, their types, and how they work.
What are Tokens in AI?
Tokens serve as the foundational elements created as artificial intelligence, machine learning, and NLP models process textual information. When large text is inputted into the AI algorithm, it breaks the text, its words, phrases, and punctuations into small blocks or tokens. This process, called tokenization, is a crucial step in preparing the data for further processing by the AI models.
Tokens are not limited to text but include various other data forms like audio and visual data. For instance, in a computer vision model, tokens would be an image segment, a group of pixels, or a single pixel only, and in voice input data, the token would be a fragment of sound. Thus, tokenization is a necessary step for AI models in processing input data.
Importance of Tokens
Tokens break the input information into small pieces, which helps the AI algorithms analyze and understand patterns in the user input and respond accordingly. For example, in chatbot development, each input word is treated as a token, which allows the NLP model to interpret the message and give an output. Tokens are even more important for more advanced models like transformers, as they collectively process all the formed tokens and enable the AI to understand the context and nuances in the large text input, which helps in various processes like translation, sentiment analysis, and content generation. Thus, tokens form the basic building blocks for the AI models and let them process various types of inputs like text, images, and audio.
Types of Tokens in AI
Tokens are of different types, on the basis of which data is separated into different pieces. Let us understand the types of tokens for text data:
Word Tokens: Each word in the text data is a separate token. For example, in the sentence, “The bird is sitting on the tree”, all the words “The”, “bird”, “is”, “ sitting”, “on”, “ the”, and “ tree”, are different tokens.
Sub Word Tokens: As the name suggests, sub-word tokens are the tokens formed of sub-parts of the words, i.e., tokens made by breaking a word into even smaller and meaningful pieces. For example, in the word “unapproachable”, “un”, “approach”, and “able” are three different tokens.
Punctuation Tokens: All the punctuation marks in the text data, like commas (,), periods (.), colons (:), question marks(?), exclamation marks (!), and more, are considered separate tokens.
Special Tokens: Special tokens are reserved symbols with predefined roles for tasks like sentence segmentation, padding, or representing out-of-vocabulary words. For example, in BERT, [CLS] marks the beginning of a sentence, [SEP] separates sentences, and [PAD] is used to pad sequences to equal lengths for model inputs.
Token Limits
Token limits are the maximum number of tokens or words that can be processed by the AI model in a single go. For example, the latest version of GPT-4 has a max token limit of 128,0000 tokens per input and a token limit of 4096 tokens per output. Token limits vary depending on various factors like computational resources, memory constraints, architectural design of the model, and so on. They impact tasks like text classification, language modeling, and machine learning. There are different ways to process large data in accordance with the token limits, like breaking down the text into smaller segments to make it fit within the token limit of a model.
Tokenization Process
Following are the steps referring to the process of tokenization:
Splitting: Breaking down the text data into smaller pieces, like words and subwords, depending upon the tokenization strategy.
Normalization: Normalization refers to converting the whole of the text or tokens into a standard form, like converting all the characters into lowercase (let’s say). This ensures consistency and removes differences that do not change the meaning of the text. Standardization can involve the following:
- Lowercasing
- Punctuation removal
- Handling special characters
Mapping: Mapping refers to providing unique identifiers or token IDs to the normalized tokens in the vocabulary, thus allowing the model to process queries efficiently. The vocabulary typically contains a finite set of tokens, words, subwords, and special tokens.
Applications of Tokenization in AI
Let us read how tokenization is used for various purposes:
Data Security: Tokenization converts the sensible data into tokens, which prevents the risks of security breaches and unauthorized access diminishes.
Text Processing: Tokenization forms the basis of NLP models. By breaking huge amounts of text data into understandable tokens, tokenization allows NLP models to interpret, analyze, and generate text responses for user queries.
Financial Transactions: The finance sector secures the financial data of the people by using tokenization. For example, by replacing the original debit card number with the tokens, data is transferred across various domains without compromising its security and reducing fraud risks.
Healthcare: Similar to the finance sector, medical records are turned into tokens by healthcare organizations to secure patient’s personal and medical information and allow safe transfer across various domains for research, diagnostic, and other purposes.
Benefits of AI Tokens
Tokenization offers many benefits that are making it lead the AI world; let us jump into some of these benefits:
Robust Data Security: As tokenization replaces the sensitive information with complex or undescriptive tokens, it ensures robust data security and locks the information while transferring it from one location to another.
Flexibility in Data Processing: Today, data is generated in huge amounts; therefore, AI systems that can process all the data efficiently, accurately, and safely are necessary. Tokenization encourages these features in AI models, thereby enhancing their scalability and flexibility to different data types.
Simplifying Compliance Processes: In highly regulated sectors such as finance and healthcare, stringent data protection mandates are in place. Tokenization serves as a valuable tool, alleviating compliance pressures by minimizing the risk of sensitive data exposure, simplifying audit procedures, and ensuring adherence to set industry standards.
Cost Efficiency: By eliminating the risks of security and data breaches, AI tokenization helps in saving huge fines and by allowing efficient storage as it breaks down huge forms of data, it also saves storage costs for the company.
Challenges that hinder the way!
AI Tokens make data processing and data transfer quite easy. However, there are some challenges that need to be looked upon for effective tokenization in AI:
Token Limits: Token limits sometimes hinder the processing of large or complex data and reduce the flexibility and efficiency of AI models.
Diversity in Languages: Different languages with different language principles require different tokenization strategies. For example, the tokenization way of English might not fit Spanish and French.
Ambiguity in Tokens: Languages contain certain complex words for which tokenization might not be straightforward and could be ambiguous.
However, as the advancements are escalating in the field of artificial intelligence, it will also affect tokenization, and we can soon expect reduced limitations of tokenization; meaning, the token limits can be minimized, which would result in scaled-up and more efficient AI models. In addition, we can expect enhanced processing tokenized speed in AI algorithms, which would enable large amounts of text to get processed quickly and without the need to cut it into smaller fragments. At last, more context-aware tokenization, which can understand idioms, sarcasm, etc., and can do multimodal processing, i.e., comprehensive AI models that can process text, images, and audio simultaneously, can be seen.
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