Understanding How Generative AI Tokenizers Work

Today’s article was inspired by a deep dive into the fascinating mechanics of Generative AI tokenizers, sparked by Andrej Karpathy’s highly technical, but insightful, two-hour lecture on the subject. For those interested in the core concepts of how tokenizers function within AI models, his lecture offers a comprehensive look at the math, mechanics, and logic behind the scenes. It serves as a great foundation for understanding how models break down text and process information. You can find a link to his lecture below.

Additionally, Karpathy uses a free online tokenizer in his lecture, which provides a clear and visual demonstration of tokenization in action. You can explore how different models break down text into tokens using this tool (link below). This interactive tool helps users visualize how generative AI models process language at the token level, making it an excellent resource for understanding these complex processes.

This article will break down the tokenizer process and discuss why it’s essential to understand the differences between models and providers when evaluating AI tools.

What Is a Tokenizer?

In simple terms, a tokenizer is the component of an AI model that splits the input text into smaller units—called tokens. These tokens can be words, parts of words, or even characters, depending on the tokenizer’s design and the language it’s working with.

Once a piece of text is split into tokens, each token is then fed into the AI model, which processes them to generate predictions or outputs. In generative AI, tokenizers are crucial for understanding and producing language because they break complex human language into manageable units for computation.

How Do Tokenizers Differ Between Models?

One important point raised in Karpathy’s lecture is how the tokenizer used by a model significantly impacts how it interprets text. Tokenizers aren’t universal across models, and different architectures tokenize language in distinct ways:

1. Word-level Tokenizers: Some early models use word-level tokenization, where each word is treated as a token. While simple, this method struggles with languages that have many variations or compound words.

2. Subword-level Tokenizers: Most modern AI models, such as GPT, use subword-level tokenization. Instead of using whole words, the text is broken into subwords, allowing the model to better handle rare or compound words. This is common in models like BERT or GPT-family models, which use Byte Pair Encoding (BPE) or other similar approaches.

3. Character-level Tokenizers: Some models operate at the character level, tokenizing individual letters or symbols. While more granular, this method requires more computational power, as each token represents a tiny portion of the text, leading to longer sequences to process.

Different tokenization approaches come with trade-offs, primarily between token efficiency and the model’s ability to understand context. Subword-level tokenization has become a popular choice for balancing token efficiency and language comprehension.

The Complexity of Token Comparisons

It’s important to highlight that not all tokens are created equal across different models and providers. This is a crucial point, especially when evaluating token pricing models from AI service providers. Some users might make decisions based solely on the cost per token, thinking that fewer tokens or a cheaper per-token price will result in cost savings. However, this is not always the case.

Here are a few factors that can complicate token comparisons:

? Token Granularity: Models with more granular tokenization methods (like character-level tokenizers) might generate more tokens for the same input text compared to subword-level tokenizers. This could result in higher token usage for the same task, potentially driving up the cost even if the per-token price is lower.

? Token Length: How text is tokenized can also impact how efficiently information is processed. For instance, a subword tokenizer might split a sentence into fewer tokens, resulting in a more concise representation that uses fewer total tokens than a word-level or character-level tokenizer.

? Model Efficiency: Different models process tokens with varying levels of efficiency. Even with similar token costs, one model might perform more effectively with fewer tokens, while another might need a larger token count to achieve the same result. This can also affect the cost.

Why Token Price Comparisons Can Be Misleading

Relying solely on token price comparisons between models or providers can be misleading. The key point here is that token prices are tied not only to the cost per token but also to how efficiently those tokens are used in processing the task.

For example, two models might charge the same per-token rate, but due to differences in how they tokenize text, one might require 20 tokens for a sentence, while another needs 30. Without understanding how each model handles tokenization, a straightforward price comparison won’t give you the full picture.

Similarly, the structure of the text being processed can affect how models tokenize and process the input, leading to varied token usage even for identical tasks. This is why it’s crucial to not make decisions solely on cost-per-token but to understand the broader context of the model’s tokenizer and performance.

Conclusion

Tokenizers are a critical but often overlooked aspect of how generative AI models function. Inspired by Karpathy’s lecture, today’s article serves as a reminder that while token price is an important factor, understanding how different models tokenize and process text is equally, if not more, important. When evaluating AI models, take into account how the tokenizer operates, as this can significantly impact both performance and cost.

For those looking to delve further, Andrej Karpathy’s lecture is an excellent resource to explore the nuances of tokenizer architecture and how it shapes modern AI systems. Additionally, the free tiktokenizer tool is a great way to visualize tokenization in action, helping you better understand how these processes impact language generation. Understanding these technical details will allow you to make better-informed decisions about which AI tools and models best fit your needs.

Links

Let's build the GPT Tokenizer, by Andrej Karpathy’s

Tiktokenizer tool