Large Language Models (LLMs) like OpenAI’s ChatGPT represent a new revolution in artificial intelligence. These models have changed how humans interact with machines to access information.
The enhanced ability of computers to understand natural language is mostly because of modern LLMS. Tokenization has been a key factor in this process, enabling models to understand human language and expand their vocabulary to respond to queries.
This article will guide you through the process of LLM tokenization and how you can enhance your model-building efforts by selecting the best tokenization library.
What is LLM Based Tokenization?
LLMs are usually trained on sequences of elements, also known as tokens, represented in a vector form rather than just sentences.
Tokenization in LLM is the process of separating a sequence of words into discrete components, called tokens, which are part of an LLM’s vocabulary. These tokens may include words, letters, or sequences of letters.
For example, consider the line from the poem:
Humpty Dumpty sat on a ____.
You can easily understand the meaning of this sentence and guess the missing word. However, machines do not inherently possess this ability and view this sentence as a series of characters or tokens.
To predict the next word, a machine needs to first tokenize the sentence and identify whether it’s a word or a letter that fills the blank space. This can be efficiently achieved using Python’s Natural Language Toolkit (NLTK) library for natural language processing (NLP).
The NLTK library enables you to build models for computer interaction with natural language. It offers a word_tokenize() method that can help you tokenize a sentence:
[‘Humpty’, ‘ Dumpty’, ‘sat’, ‘on’, ‘a’]
Each element in this list is a token. Every token in this list is associated with a unique ID, which is usually a number that maps to the token embedding. These tokens are then fed to the LLM, which determines the next word in this sequence.
Let’s explore how the OpenAI tokenizer converts this sentence into a list of IDs:
[49619, 1625, 65082, 1625, 7731, 389, 264]
When you enter the given example sentence into the tokenizer, it produces these IDs. Note that the sequence ‘pty’ appears twice, which is why the ID 1625 is repeated in the output.
Why Does Tokenization Matter?
Tokenization significantly impacts an LLM’s behavior, as it determines the model’s vocabulary. The vocabulary helps the model process inputs and predict outputs. Most of the issues encountered with LLMs can be attributed to how the text is tokenized.
Training models directly with raw text documents might generate unexpected outcomes, such as spelling errors or mishandling of different languages. With the right tokenization techniques, you can efficiently manage these issues and train your model to produce accurate responses.
Relationship between Tokenization & Vocabulary
For large language models, vocabulary determines the type of data on which the model is trained and the output it produces. When designing a model, you must tailor the vocabulary according to your business needs, ensuring the model’s responses align with organizational requirements.
Tokenization helps in enhancing the model’s vocabulary by introducing new words with unique IDs. These new tokens are added to the LLM’s vocabulary. As a result, the model becomes capable of recognizing and responding to new phrases, improving the quality of its output.
To align your LLM with business-specific needs, it is essential to incorporate in-house and cloud data into the training process. However, the data required for training might be distributed across various sources.
Consolidating this data from dispersed sources into a single destination makes it easier for you to access it and train your LLM with relevant information. Manually integrating data from multiple sources into a destination can be complex. To simplify the process, you can choose no-code tools like Airbyte. We will explore the specific features of this tool in the later section.
Real-Time Tokenization Process
Real-time tokenization is the process of instantly converting text into tokens to train LLMs and produce accurate responses. This is crucial in real-world applications where LLMs must provide prompt responses. If the response is not fast, alternative models might be considered that can effectively produce quick and precise responses.
The real-time tokenization process involves the following steps:
- It starts by breaking down the incoming information into tokens that are already defined in the LLM’s vocabulary.
- Each token is assigned a unique ID corresponding to its position within the vocabulary.
- By providing extra tokens with the input, you can enhance the model's processing capabilities so that the model understands the input text.
Popular Tokenization Libraries/Models
Here are the most prominent tokenization libraries:
Hugging Face Tokenizer
Hugging Face Tokenizer is one of the most widely used tokenizer libraries. It is a robust tool designed for research and production purposes. With Tokenizer’s Rust implementation, it takes less than 20 seconds to tokenize up to a gigabyte of data on the server’s CPU. It also supports full alignment tracking, mapping the original value of each segment of text to its corresponding token.
SentencePiece
SentencePiece is an unsupervised text tokenizer that utilizes the unigram language model and subword units like byte-pair-encoding (BPE). It is useful for various applications, especially for neural network-based text generation systems or in cases where the model vocabulary is defined prior to training. One of the main benefits of SentencePiece is that it trains tokenization models using sentences without requiring pre-tokenization.
OpenAI Tiktoken
OpenAI’s Tiktoken is an open-source project used to tokenize textual data. This library's encoding parameters specify how text is converted into tokens. Some of the most common encoding schemes include cl100k_base, p50k_base, and r50k_base. These parameters demonstrate the methods for splitting words, distributing spaces, and handling non-English characters.
NLTK Tokenize
In the Python ecosystem, the Natural Language Toolkit, or NLTK, is a collection of libraries and programs for natural language processing tasks. It contains an LLM tokenizer package with multiple methods that enable you to tokenize any text. You must ensure that you are not using encoded strings before getting started with the tokenization process.
Tokenization's Impact on Model Performance
According to a recent study, using English tokens for multilingual LLMs is not optimal. Tasks such as summarization and translation may experience performance degradation and up to 68% increase in response time when using English-only tokens. This is why selecting an appropriate tokenization vocabulary becomes necessary to enhance the outcomes of LLMs.
The choice of tokenizer library influences the LLM's performance. This study showed that the SentencePiece library outperformed the Hugging Face tokenizer library.
Another factor that influences the LLM performance is the algorithm used for tokenizing content. The byte-pair-encoding (BPE) algorithms are a good choice for mono- and multilingual models.
For English-centric models, a vocabulary size of approximately 33k is optimal. On the other hand, models designed to be compatible with up to five languages generally require a vocabulary that is three times larger.
Best Practices for Efficient Tokenization
When building your own LLM to automate your workflow, there are some best practices related to tokenization to help you achieve the best possible outcome. Here are some of the best practices:
- You must choose an appropriate tokenization library to help train your model. The library should support the needs of your model and workflow. Apart from providing good support for your chosen tokenization methods, it should also integrate well with your system architecture.
- The tokenization algorithm that you select significantly affects your model performance. Common tokenization algorithms include byte-pair-encoding (BPE), Unigram tokenization, WordPiece tokenization, and SentencePiece LLM tokenization.
- The vocabulary size impacts your model performance. A larger vocabulary can enhance the model’s ability to produce accurate responses. However, increasing the vocabulary size also increases the computational costs.
Future Trends in LLM Tokenization
The performance of LLMs has been significantly improved and is supposed to improve further in the future. Here are some of the key anticipated changes in LLM tokenization:
- Multilingual Tokenization: Advanced tokenization techniques are likely to enhance multilingual tokenization with models supporting multiple languages. These techniques will enable language models to effectively process languages from different parts of the world.
- Tokenization Algorithms: The development and inclusion of different LLM tokenization algorithms will improve how LLM tokenizers work, boosting the overall performance of the model.
- Token Limit: For OpenAI models, the token limits have been increasing over time. The OpenAI GPT-4 model currently supports 8,192 tokens of context. On the other hand, its advanced version, the GPT-4 32k model, supports up to 32,768 tokens. This helps in improving the text in a single conversion, allowing you to enter a more descriptive prompt while describing your problem.
Enhance Your LLM Applications with Airbyte
Airbyte is a data integration tool that allows you to move data from several prominent data sources to the destination of your choice. It offers 350+ pre-built data connectors that are capable of handling structured and unstructured data. These connectors allow you to extract your data and consolidate it into a centralized repository.
If the connector you are looking for is unavailable in the connector options, you can utilize Airbyte’s Connector Development Kit (CDK) to develop a custom connector of your choice.
Let’s explore some key features of Airbyte:
- Modern GenAI Workflow Support: Airbyte enables you to integrate semi-structured and unstructured data into popular vector databases like Pinecone, Milvus, and Weaviate to simplify AI workflows. It allows you to transform and store data in a single operation with the help of RAG-specific transformations like chunking and embedding.
- Change Data Capture: The CDC functionality allows you to effectively capture the changes made to the source data and reflect them at the destination without reloading the entire dataset.
- Extensive Python Library: Airbyte offers the PyAirbyte library, which enables you to develop and manage data pipelines using Python. This open-source library provides all the Airbyte connectors and makes them available in Python for developing data-driven applications.
- Data Security: Airbyte offers multiple security features to keep your data secure. It is compliant with prominent security certifications and standards, including SOC 2, GDPR, HIPAA, and ISO 27001.
- Flexible Deployment Options: It provides flexible deployment options, offering self-hosted, cloud, and hybrid solutions.
By leveraging Airbyte's beneficial features, you can save time and resources. This allows you to focus on developing your model instead of spending effort on data integration tasks.
Summary
Understanding LLM tokenization is the first step toward developing effective large language models. There are multiple libraries and algorithms available that can help you develop an LLM tokenizer that efficiently converts user input into tokens.
In addition to this information, you must also ensure you follow the best practices for tokenization. This can enable you to improve your LLM’s performance, ensuring it responds to your queries and processes information effectively.
FAQs
What is tokenizer LLM?
A tokenizer in an LLM is a program or software that breaks down textual information into smaller, easily manageable units called tokens.
What is tokenization in LLM Medium?
Tokenization is the process of converting text into tokens. It helps LLMs to process and understand human language to produce accurate and relevant answers.
How do LLMs work with tokens?
LLMs use tokenizers to process input text by dividing it into words, subwords, or characters, known as tokens. These tokens are then used to train the LLMs.
What is the purpose of tokenization in the processing of text by LLMs?
Tokenization breaks down text into smaller components, or tokens, which are then converted into integer IDs and stored in an array. These IDs refer to the model’s vocabulary, enabling LLMs to understand inputs and produce appropriate responses.
What is a LLM in NLP?
An LLM, or large language model, is an artificial intelligence model that can understand and process natural language to produce output. Generally, computers aren’t equipped to tackle problems in human language. This is where LLMs can help and perform natural language processing (NLP) tasks.
What is the difference between a token and a word in LLM?
In LLMs, a token is a basic unit of input or output that may represent a word, character, or a sequence of characters. Unlike words that are a distinct unit in a language, tokens can include segmented pieces of text that can help you train LLMs.