A Vector Embedding corresponds to representing a word or token as a vector of numbers in a high-dimensional space. This allows mathematical operations to reflect semantic meaning.
Concept
If we imagine a vector where dimensions correspond to features like “has a tail”, “is eatable”, or “is a pet”, words with similar properties will have similar vector representations.
- Example: “Apple” and “Banana” will have similar values for “is eatable” and “makes sound” (low), whereas “Dog” and “Cat” will share high values for “has a tail”.
Semantic Properties
Well-trained vector embeddings exhibit remarkable properties:
- Similarity: The magnitude of the difference between vectors indicates semantic distance. “Man” and “Woman” are closer than “Semiconductor” and “Earthworm”.
- Arithmetic: You can perform operations like
King + Woman - Man. The resulting vector is closest toQueen.
Role in LLMs
In the context of Large Language Models, Token Embeddings typically represent the third step in the workflow, following tokenization and the conversion of tokens to token IDs.
Comparison with One-Hot Encoding
Unlike simpler methods like One-Hot Encoding or random number assignment, embeddings convert individual tokens into continuous vector representations that capture semantic meaning.
Why it Matters
- Preserving Meaning: Words like “cat” and “kitten” are semantically related. Random IDs or sparse vectors (One-Hot) fail to capture this relationship.
- Analogy: Similar to how Convolutional Neural Networks (CNNs) exploit spatial relations in images (e.g., eyes are close to the nose), LLMs use embeddings to exploit semantic relations in text.
