Explained: Tokens and Embeddings in LLMs

Introduction

Large Language Models (LLMs) like GPT-4 Turbo and Claude 2.1 process text using tokens and embeddings—numerical representations that enable computers to understand human language. This guide breaks down these concepts, their creation methods, and their role in NLP systems.

What Are Tokens?

Tokens are the smallest units of text in NLP, created by splitting sentences into manageable parts. For example:

• Sentence: "It’s over 9000!"
• Tokens: ["It's", "over", "9000!"]

Why Tokenize?

1. Simplify complex text for analysis.
2. Enable numerical processing (computers don’t understand raw text).
3. Support NLP tasks like translation or sentiment analysis.

Tokenization Methods

1. White Space Tokenization

Splits text by spaces:

"ChatGPT is amazing".split() → ["ChatGPT", "is", "amazing"]  

2. Subword Tokenization (Used in LLMs)

Breaks words into smaller units:

• Example: "9000!" → ["9000", "!"]
• Purpose: Handles rare words and reduces vocabulary size.

Byte-Pair Encoding (BPE)

Popular in models like GPT-2:

from transformers import GPT2Tokenizer  
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")  
tokenizer.encode("Hello, world!") → [31373, 10144, 33]  

From Tokens to Embeddings

Computers need numbers, not text. Token IDs are converted into embeddings—dense vectors capturing semantic meaning.

Embedding Techniques

1. Word2Vec: Predicts words based on context.
2. BERT: Generates context-aware embeddings.

Example: BERT Embeddings

from transformers import BertModel  
model = BertModel.from_pretrained("bert-base-uncased")  
embeddings = model(**inputs).last_hidden_state  

Output: A 768-dimensional vector per token.

Why Embeddings Matter

1. Capture context: The word "bank" has different embeddings in "river bank" vs. "bank account."
2. Enable advanced tasks: Power chatbots, search engines, and more.
3. Optimize model performance: Better embeddings = smarter LLMs.

FAQs

1. How many tokens fit in GPT-4 Turbo’s context?

128K tokens (~100K words).

2. Are embeddings unique to each model?

Yes—BERT’s embeddings differ from GPT’s due to training data and architecture.

3. Can I create custom tokenizers?

Absolutely, but subword methods (e.g., BPE) are standard for efficiency.

👉 Explore advanced NLP techniques

Key Takeaways

• Tokens chunk text for processing.
• Embeddings turn tokens into rich numerical data.
• Subword methods (like BPE) balance vocabulary size and flexibility.

For deeper dives, reach out at [email protected].

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