The Role of Tokenization in LLMs: Does It Matter?

Large language models (LLMs) like GPT-3, GPT-4, or Google's BERT have become a big part of how artificial intelligence (AI) understands and processes human language. But behind these models' impressive abilities is a hidden process that's easy to overlook: tokenization. This article will explain what tokenization is, why it's so important, and whether or not it can be avoided.

Imagine you're reading a book, but instead of words and sentences, the entire text is just one giant string of letters without spaces or punctuation. It would be hard to make sense of anything! That's what it would be like for a computer to process raw text. To make language understandable to a machine, the text needs to be broken down into smaller, digestible parts — these parts are called tokens.

What Is Tokenization?

Tokenization is the process of splitting text into smaller chunks that are easier for the model to understand. These chunks can be:

• Words: Most natural unit of language (e.g., "I", "am", "happy").
• Subwords: Smaller units that help when the model doesn't know the whole word (e.g., "run", "ning" in "running").
• Characters: In some cases, individual letters or symbols (e.g., "a", "b", "c").

Why Do We Need Tokens?

Let's take an example sentence:

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

A computer sees this sentence as a long sequence of letters: Thequickbrownfoxjumpsoverthelazydog.

The computer can't understand this unless we break it down into smaller parts or tokens. Here's what the tokenized version of this sentence might look like:

1. Word-level tokenization:

• ["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"]

2. Subword-level tokenization:

• ["The", "qu", "ick", "bro", "wn", "fox", "jump", "s", "over", "the", "lazy", "dog"]

3. Character-level tokenization:

• ["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"]

The model then learns from these tokens, understanding patterns and relationships. Without tokens, the machine wouldn't know where one word starts and another ends or what part of a word is important.

How Tokenization Works in LLMs

Large language models don't "understand" language the way humans do. Instead, they analyze patterns in text data. Tokenization is crucial for this because it helps break the text down into a form that's easy for a model to process.

Most LLMs use specific tokenization methods:

Byte Pair Encoding (BPE)

This method combines characters or subwords into frequently used groups. For example, "running" might be split into "run" and "ning." BPE is useful for capturing subword-level patterns.

WordPiece

This tokenization method is used by BERT and other models. It works similarly to BPE but builds tokens based on their frequency and meaning in context.

SentencePiece

This is a more general approach to tokenization that can handle languages without clear word boundaries, like Chinese or Japanese.

How Tokenization Works in LLMs

The way text is broken down can significantly affect how well an LLM performs. Let's dive into some key reasons why tokenization is essential:

Efficient Processing

Language models need to process massive amounts of text. Tokenization reduces text into manageable pieces, making it easier for the model to handle large datasets without running out of memory or becoming overwhelmed.

Handling Unknown Words

Sometimes, the model encounters words it hasn't seen before. If the model only understands entire words and comes across something unusual, like "supercalifragilisticexpialidocious," it might not know what to do. Subword tokenization helps by breaking the word down into smaller parts like "super," "cali," and "frag," making it possible for the model to still understand.

Multi-Lingual and Complex Texts

Different languages structure words in unique ways. Tokenization helps break down words in languages with different alphabets, like Arabic or Chinese, and even handles complex things like hashtags on social media (#ThrowbackThursday).

An Example of How Tokenization Helps

Let's look at how tokenization can help a model handle a sentence with a complicated word.

Imagine a language model is given this sentence:

"Artificial intelligence is transforming industries at an unprecedented rate."

Without tokenization, the model might struggle with understanding the entire sentence. However, when tokenized, it looks like this:

Tokenized version (subwords):

• ["Artificial", "intelligence", "is", "transform", "ing", "industr", "ies", "at", "an", "unprecedented", "rate"]

Now, even though "transforming" and "industries" might be tricky words, the model breaks them into simpler parts ("transform", "ing", "industr", "ies"). This makes it easier for the model to learn from them.

Challenges in Tokenization

While tokenization is essential, it's not perfect. There are a few challenges:

Languages Without Spaces

Some languages, like Chinese or Thai, don't have spaces between words. This makes tokenization difficult because the model has to decide where one word ends and another begins.

Ambiguous Words

Tokenization can struggle when a word has multiple meanings. For example, the word "lead" could mean a metal or being in charge. The tokenization process can't always determine the correct meaning based on tokens alone.

Rare Words

LLMs often encounter rare words or invented terms, especially on the internet. If a word isn't in the model's vocabulary, the tokenization process might split it into awkward or unhelpful tokens.

Can We Avoid Tokenization?

Given its importance, the next question is whether tokenization can be avoided.

In theory, it's possible to build models that don't rely on tokenization by directly working at the character level (i.e., treating every single character as a token). But there are drawbacks to this approach:

Higher Computational Costs

Working with characters requires much more computation. Instead of processing just a few tokens for a sentence, the model would need to process hundreds of characters. This significantly increases the model's memory and processing time.

Loss of Meaning

Characters don't always hold meaning on their own. For example, the letter "a" in "apple" and "a" in "cat" are the same, but the words have completely different meanings. Without tokens to guide the model, it can be harder for the AI to grasp context.

That being said, some experimental models are trying to move away from tokenization. But for now, tokenization remains the most efficient and effective way for LLMs to process language.

Conclusion

Tokenization might seem like a simple task, but it's fundamental to how large language models understand and process human language. Without it, LLMs would struggle to make sense of text, handle different languages, or process rare words. While some research is looking into alternatives to tokenization, for now, it's an essential part of how LLMs work.

The next time you use a language model, whether it's answering a question, translating a text, or writing a poem, remember: it's all made possible by tokenization, which breaks down words into parts so that AI can better understand and respond.

Key Takeaways

• Tokenization is the process of breaking text into smaller, more manageable pieces called tokens.
• Tokens can be words, subwords, or individual characters.
• Tokenization is crucial for models to efficiently process text, handle unknown words, and work across different languages.
• While alternatives exist, tokenization remains an essential part of modern LLMs.