Embedding Token Behavior: How Tokens Become High-Dimensional Vectors

Embedding token behavior describes the transformation of discrete tokens into continuous numerical representations known as embeddings. These embeddings form the basis of how AI models understand, compare, and reason about text. By learning patterns during pretraining, models map tokens into high-dimensional space where semantic relationships can be encoded mathematically.

Each token receives an embedding vector—often hundreds or thousands of dimensions long. These vectors capture semantic and syntactic properties learned from massive training corpora. For example, tokens related to finance may cluster together, while tokens associated with emotions or locations form other distinct patterns. This spatial structure allows models to interpret similarity, perform analogies, and infer relationships between words.

The embedding process also depends heavily on tokenization. If a word is broken into subwords, each fragment receives its own embedding. The model then combines these fragments internally to create a coherent representation. This explains why fragmented tokens sometimes behave differently from whole words—they begin with separate embeddings before being unified through attention layers.

Embedding drift may occur during inference. This refers to subtle shifts in vector interpretation based on surrounding context. Because embeddings are influenced by adjacent tokens, a word’s meaning may change depending on its placement within a sentence. This dynamic behavior enables nuanced reasoning but also introduces unpredictability in ambiguous text.

Understanding embedding token behavior provides insight into why models sometimes generate surprising associations or misinterpret uncommon terms. The quality of embeddings, shaped heavily by tokenization choices, dictates how effectively a model can process and generate language at a conceptual level.