Input Tokens

Input tokens are the discrete units of text or code fed into a language model. These tokens are processed to generate an output. Tokenization is crucial for model performance and efficiency.

Detailed explanation

Input tokens are the fundamental building blocks that large language models (LLMs) use to understand and process information. Before an LLM can work with text or code, it must first break it down into these smaller units, a process known as tokenization. The choice of tokenization method and the resulting tokens significantly impact the model's performance, efficiency, and overall capabilities.

What is Tokenization?

Tokenization is the process of splitting a sequence of text or code into smaller, meaningful units called tokens. These tokens can be words, parts of words (subwords), or even individual characters. The goal of tokenization is to create a representation of the input that the LLM can effectively process and learn from.

Different tokenization algorithms exist, each with its own strengths and weaknesses. Some common methods include:

  • Word-based tokenization: This is the simplest approach, where the text is split into individual words based on spaces and punctuation. While easy to implement, it can lead to a large vocabulary size, especially when dealing with languages with rich morphology or specialized domains with many technical terms. It also struggles with out-of-vocabulary (OOV) words, which are words not seen during the model's training.

  • Character-based tokenization: This method splits the text into individual characters. It has a very small vocabulary size and can handle OOV words, but it loses much of the semantic meaning of the text, making it harder for the model to learn long-range dependencies.

  • Subword tokenization: This approach strikes a balance between word-based and character-based tokenization. It breaks words into smaller units (subwords) based on statistical analysis of the training data. This allows the model to handle OOV words by breaking them down into known subwords, while also retaining more semantic meaning than character-based tokenization. Popular subword tokenization algorithms include Byte Pair Encoding (BPE) and WordPiece.

Why are Input Tokens Important?

The choice of tokenization method and the resulting input tokens have a significant impact on several aspects of LLM performance:

  • Vocabulary Size: The number of unique tokens in the vocabulary directly affects the size of the model's embedding layer, which maps each token to a vector representation. A larger vocabulary requires a larger embedding layer, increasing the model's memory footprint and computational cost.

  • Model Performance: The quality of the tokens influences the model's ability to understand and process the input text. Well-chosen tokens can capture the semantic meaning of the text more effectively, leading to better performance on downstream tasks such as text classification, machine translation, and text generation.

  • Handling Out-of-Vocabulary (OOV) Words: Subword tokenization methods are particularly effective at handling OOV words, which are words not seen during the model's training. By breaking down OOV words into known subwords, the model can still process and understand them to some extent.

  • Computational Efficiency: The number of tokens in the input sequence affects the computational cost of processing the sequence. Longer sequences require more computation, so it's important to choose a tokenization method that balances vocabulary size and sequence length.

Input Tokens in Practice

When working with LLMs, it's important to understand how the model tokenizes the input text. Most LLMs have a built-in tokenizer that is used to convert the input text into a sequence of tokens. The tokenizer is typically trained on a large corpus of text and is optimized for the specific language and domain of the model.

Before feeding text to an LLM, developers need to tokenize it using the model's tokenizer. The resulting tokens are then converted into numerical IDs, which are used as input to the model. The model processes these IDs and generates a sequence of output tokens, which are then detokenized to produce the final output text.

The number of input tokens also directly impacts the cost of using many LLM APIs. These APIs often charge based on the number of input and output tokens used in a request. Understanding tokenization helps developers optimize their prompts and reduce costs.

Examples

Let's consider a simple example to illustrate the concept of tokenization. Suppose we have the following sentence:

"The quick brown fox jumps over the lazy dog."

  • Word-based tokenization: The tokens would be: ["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog", "."]

  • Character-based tokenization: The tokens would be: ["T", "h", "e", " ", "q", "u", "i", "c", "k", " ", "b", "r", "o", "w", "n", " ", "f", "o", "x", " ", "j", "u", "m", "p", "s", " ", "o", "v", "e", "r", " ", "t", "h", "e", " ", "l", "a", "z", "y", " ", "d", "o", "g", "."]

  • Subword tokenization (using BPE): The tokens might be: ["The", "quick", "brown", "fox", "jump", "s", "over", "the", "lazy", "dog", "."] (Note: The exact subwords will depend on the training data.)

As you can see, each tokenization method produces a different set of tokens. The choice of method depends on the specific application and the characteristics of the language being processed.

Further reading

Related Terms

A/B Testing

Abstraction Hierarchy

Action Execution