Token Embedding
Token Embedding is the process of converting discrete tokens (words, subwords, or characters) into dense, continuous vector representations in a high-dimensional space. These vectors capture semantic and syntactic relationships, enabling neural networks to process linguistic data numerically.
Core Mechanics
- Vector Representation: Each token is mapped to a fixed-size vector (embedding) where dimensions correspond to learned features.
- Semantic Proximity: Tokens with similar meanings or contexts are positioned closer together in the embedding space.
- Input Layer: In Transformer architectures, token embeddings serve as the primary input, often combined with Positional Encoding to retain sequence order.
Integration with GPT Architecture
Based on recent analysis of Generative Pre-trained Transformers:
- Foundational Role: Token embeddings are the initial step in the GPT pipeline, transforming raw text into a format suitable for matrix operations within the Attention Mechanism.
- Contextual Learning: While static embeddings exist, modern LLMs utilize contextual embeddings that evolve through layers, allowing the same token to have different representations based on surrounding context.
- Visual Explanation: The interaction between token embeddings and attention heads is critical for understanding how GPT generates coherent text; see How GPT Works: Token Embedding and Attention Mechanisms Explained for a detailed breakdown of this process.