Tokenization is a fundamental process in the functioning of large language models (LLMs). It is the step where raw text is converted into a sequence of tokens, which are essentially the building blocks that LLMs understand and manipulate.
Understanding tokenization is crucial when working with LLMs because it is intricately tied to many of the peculiar behaviors and limitations observed in these models. Issues with spelling, handling of non-English languages, and even certain security concerns can often be traced back to how tokenization is handled.
Tokenization is the process of converting a string of text into a sequence of tokens. These tokens are then used by the LLM to understand and generate text. In a naive tokenization approach, we might simply split text into words or characters, but modern LLMs use more sophisticated methods.
In the simplest form of tokenization, each character in a text is treated as a token. This means that the text “hi there” would be broken down into individual characters ‘h’, ‘i’, ‘ ‘, ‘t’, ‘h’, ‘e’, ‘r’, ‘e’, each assigned a unique integer token. This approach, however, is quite limited and inefficient for large language models.
Tokens are not used directly by the LLM. Instead, they are passed through an embedding table that converts each token into a vector of real numbers. These vectors are then used as input for the LLM. The embedding table is a matrix where each row corresponds to a token and contains trainable parameters that are adjusted during the model’s learning process.
State-of-the-art LLMs use advanced tokenization schemes that go beyond character-level tokenization. These schemes involve chunking text into larger pieces and using algorithms to construct a vocabulary of these chunks. One such algorithm is Byte Pair Encoding (BPE), which we will explore in detail.
BPE is a method used to construct token vocabularies by iteratively merging the most frequently occurring pairs of characters or bytes. For example, if ‘aa’ is the most common pair in a dataset, it would be merged into a single token, reducing the sequence length. This process is repeated until a desired vocabulary size is reached.
The GPT-2 paper introduced BPE in the context of LLMs. It describes how tokenization works in their model, with a vocabulary size of 50,257 tokens and a context size of 1,024 tokens. This means that at any given time, the model can consider a sequence of up to 1,024 tokens to generate the next piece of text.
Building a tokenizer from scratch allows for a deeper understanding of the process. By implementing BPE ourselves, we can see exactly how tokens are created and how they influence the performance and behavior of an LLM.
Tokenization is not without its complexities. It can introduce issues such as difficulty in spelling tasks, problems with non-English languages, and challenges in handling programming languages. Understanding these complexities is essential for effectively working with LLMs.
A live demonstration of tokenization can be illustrative. For example, using a web application that tokenizes input text in real-time can show how different strings are broken down into tokens and how this affects the processing by an LLM.
Tokenization is a critical yet complex part of working with LLMs. It influences many aspects of a model’s behavior and performance. By understanding tokenization, we can better grasp the capabilities and limitations of these powerful models and work towards improving them.