Large Language Models (LLMs) have revolutionized the field of artificial intelligence, enabling machines to understand and generate human-like text with remarkable proficiency. But how do these sophisticated systems actually process and understand language? The answer lies in three fundamental components that serve as the foundation of every LLM: tokens, embeddings, and vocabulary. Understanding these building blocks is crucial for anyone looking to grasp how modern AI language systems work.
What Are Tokens?
Tokens are the smallest units of text that an LLM can process. Think of them as the “words” that the model actually sees, though they don’t always correspond exactly to human words. The process of breaking down text into tokens is called tokenization, and it’s the very first step in how an LLM processes any input.
Types of Tokenization
Word-level tokenization splits text at word boundaries, treating each word as a separate token. While intuitive, this approach has limitations when dealing with rare words, different languages, or morphologically rich languages.
Subword tokenization has become the standard approach for modern LLMs. Popular methods include:
- Byte Pair Encoding (BPE): Starts with individual characters and iteratively merges the most frequent pairs
- WordPiece: Similar to BPE but uses a different merging criterion based on likelihood
- SentencePiece: Treats text as a sequence of characters and doesn’t require pre-tokenization
For example, the word “tokenization” might be split into tokens like [“token”, “ization”] or [“tok”, “en”, “ization”] depending on the tokenizer’s vocabulary.
Why Subword Tokenization Matters
Subword tokenization offers several advantages:
- Handling rare words: Even if a word hasn’t been seen during training, its subword components likely have been
- Morphological awareness: The model can understand relationships between related words (run, running, runner)
- Multilingual capability: Subwords can capture patterns across different languages
- Fixed vocabulary size: Unlike word-level tokenization, the vocabulary size remains manageable
Understanding Embeddings
Once text is tokenized, each token needs to be converted into a numerical representation that the neural network can process. This is where embeddings come into play. An embedding is a dense vector representation of a token, typically containing hundreds or thousands of dimensions.
The Magic of Vector Representations
Embeddings capture semantic relationships between tokens in a high-dimensional space. Tokens with similar meanings tend to have similar vector representations, and the geometric relationships between vectors often reflect linguistic relationships.
For instance, the vector arithmetic “king – man + woman ≈ queen” famously demonstrates how embeddings can capture analogical relationships. More practically, words like “happy” and “joyful” would have embeddings that are close to each other in the vector space.
How Embeddings Are Learned
In modern LLMs, embeddings are typically learned as part of the training process rather than being pre-computed. The model adjusts these vector representations to minimize its prediction errors, gradually learning to encode meaningful semantic information.
Contextual embeddings are a key innovation in modern LLMs. Unlike static embeddings where each token always has the same representation, contextual embeddings change based on the surrounding context. The word “bank” would have different embeddings in “river bank” versus “savings bank.”
Positional Embeddings
Since neural networks don’t inherently understand sequence order, LLMs use positional embeddings to encode information about where each token appears in the sequence. These can be:
- Absolute positional embeddings: Encode the exact position of each token
- Relative positional embeddings: Encode the relative distances between tokens
- Rotary Position Embedding (RoPE): A more recent approach that encodes position through rotation in the embedding space
The Role of Vocabulary
The vocabulary is the complete set of all possible tokens that an LLM can recognize and generate. It’s essentially the model’s “dictionary” and plays a crucial role in determining the model’s capabilities and limitations.
Vocabulary Construction
Building an effective vocabulary involves several considerations:
Coverage: The vocabulary should cover the expected input text well, minimizing the number of unknown tokens. This involves analyzing large text corpora to identify the most useful tokens.
Size constraints: Larger vocabularies allow for more precise representation but require more computational resources. Most modern LLMs use vocabularies ranging from 30,000 to 100,000+ tokens.
Special tokens: Vocabularies include special tokens for various purposes:
<unk>for unknown tokens<pad>for padding sequences to equal length<bos>and<eos>for beginning and end of sequence<mask>for masked language modeling tasks
Multilingual Considerations
Modern LLMs often need to handle multiple languages, which presents unique vocabulary challenges. Strategies include:
- Shared vocabularies: Using subword tokenization to create vocabularies that work across languages
- Language-specific tokens: Including tokens that capture language-specific patterns
- Script mixing: Handling texts that mix different writing systems
How It All Works Together
The interaction between tokens, embeddings, and vocabulary creates a powerful system for language understanding:
- Input processing: Raw text is tokenized according to the model’s vocabulary
- Embedding lookup: Each token is converted to its corresponding embedding vector
- Contextual processing: The transformer architecture processes these embeddings, allowing each token’s representation to be influenced by its context
- Output generation: The model predicts the next token by computing probabilities over the entire vocabulary
The Attention Mechanism
The attention mechanism is what allows embeddings to become truly contextual. It enables each token to “attend” to other tokens in the sequence, mixing their representations to create context-aware embeddings. This is what allows modern LLMs to understand complex relationships and dependencies in text.
Practical Implications
Understanding these building blocks has practical implications for working with LLMs:
Token limits: Most LLMs have maximum context lengths defined in tokens, not words. Understanding tokenization helps you estimate how much text you can process.
Vocabulary gaps: If your domain uses specialized terminology not well-represented in the training vocabulary, the model may struggle with these concepts.
Multilingual performance: The vocabulary construction affects how well the model handles different languages.
Fine-tuning considerations: When adapting LLMs for specific domains, you might need to consider vocabulary expansion or embedding initialization strategies.
The Future of LLM Building Blocks
Research continues to evolve these fundamental components:
Dynamic vocabularies: Methods for adapting vocabularies to new domains or languages without full retraining
More efficient embeddings: Techniques to reduce the memory footprint of embeddings while maintaining performance
Better tokenization: New approaches that might better capture linguistic structure or handle multilingual text
Retrieval-augmented architectures: Systems that combine parametric knowledge in embeddings with external knowledge bases
Conclusion
Tokens, embeddings, and vocabulary form the foundational layer upon which all LLM capabilities are built. Tokens provide the basic units of processing, embeddings encode semantic meaning in vector space, and vocabulary defines the scope of what the model can understand and generate.
These seemingly simple components work together to enable the remarkable language understanding capabilities we see in modern AI systems. As LLMs continue to evolve, innovations in tokenization, embedding techniques, and vocabulary construction will likely drive further improvements in their capabilities.
Understanding these building blocks not only satisfies curiosity about how LLMs work but also provides practical insights for anyone working with these powerful tools. Whether you’re developing applications, fine-tuning models, or simply trying to get better results from AI systems, knowledge of tokens, embeddings, and vocabulary will serve you well in the age of large language models.
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