Tokenization: The Foundation of LLMs

Tokenization: The Foundation of LLMs

In our previous article, we introduced the concept of Large Language Models (LLMs) and their growing importance in Natural Language Processing (NLP). In this article, we’ll dive deeper into the world of tokens and embeddings, which are essential components of LLMs. Tokenization is the process of breaking down text into individual tokens, which can be words, sub-words, characters, or even bytes.

Tokenization is a crucial step in preparing text data for LLMs. It allows the model to understand the structure and meaning of the text, which is essential for generating coherent and meaningful output. There are different types of tokenization, including word-level tokenization, sub-word level tokenization, and character-level tokenization.

How Tokenizers Prepare Inputs to LLMs

LLM tokenizers prepare inputs by converting text into a numerical representation that the model can understand. This process involves several steps, including:

1. Text Preprocessing:

The text is cleaned and preprocessed to remove punctuation, special characters, and other unnecessary elements.

2. Tokenization:

The text is broken down into individual tokens, which can be words, subwords, characters, or bytes.

3. Encoding:

The tokens are encoded into a numerical representation, such as integers or vectors, that the model can understand.

Token Embeddings: Representing Tokens in Vector Space

Token embeddings are a crucial component of LLMs, allowing the model to capture the nuances of language. These embeddings are learned during training and represent each token in a high-dimensional vector space. Token embeddings enable the model to understand the context and meaning of each token, which is essential for generating coherent and meaningful text.

Comparing Trained LLM Tokenizers

Different LLM tokenizers have distinct properties and characteristics. For example:

1. Word-Level Tokenizers:

These tokenizers break down text into individual words. They are simple and efficient but may not capture the nuances of language.

2. Subword-Level Tokenizers:

These tokenizers break down text into subwords, which are smaller units of words. They can capture the nuances of language but may be more complex and computationally expensive.

from transformers import AutoTokenizer

# Load a pretrained subword-level tokenizer (e.g., BERT or GPT)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Input text
text = "Tokenization is a key component of NLP."

# Tokenize the text into subwords
tokens = tokenizer.tokenize(text)

# Convert tokens into input IDs (numerical representation)
input_ids = tokenizer.convert_tokens_to_ids(tokens)

# Display results
print("Original Text:", text)
print("Subword Tokens:", tokens)
print("Input IDs:", input_ids)
3. Character-Level Tokenizers:

Original Text: Tokenization is a key component of NLP.
Subword Tokens: ['token', '##ization', 'is', 'a', 'key', 'component', 'of', 'nl', '##p', '.']
Input IDs: [19204, 3989, 2003, 1037, 3145, 6922, 1997, 17953, 2361, 1012]

3. Character-Level Tokenizers:

These tokenizers break down text into individual characters. They can capture the nuances of language but may be more complex and computationally expensive.

Token Embeddings in Practice

Token embeddings have numerous applications in NLP tasks, such as:

Text Classification:

Token embeddings can be used to classify text into different categories, such as spam vs. non-spam emails.

Sentiment Analysis:

Token embeddings can be used to analyze the sentiment of text, such as positive vs. negative reviews.

Language Translation:

Token embeddings can be used to translate text from one language to another.

Text Summarization:

Token embeddings can be used to summarize long pieces of text into shorter summaries.

Stay tuned for Next Part, where we’ll explore the mechanics of attention mechanisms, the role of transformers, and how these elements come together to power modern LLMs!