Visualizing LLM Embeddings: The Geometry of Meaning
Introduction: Words as Numbers How do language models understand meaning? The answer lies in embeddings: representing words, sentences, and entire documents as vectors of numbers in high-dimensional space. In this space: Similar words cluster together Analogies emerge as geometric relationships Meaning becomes computable through vector arithmetic Let’s visualize this invisible geometry where meaning is distance. From Words to Vectors Traditional Approach: One-Hot Encoding graph TB A[Vocabulary:cat, dog, king, queen, apple] A --> B[cat = 1,0,0,0,0] A --> C[dog = 0,1,0,0,0] A --> D[king = 0,0,1,0,0] A --> E[queen = 0,0,0,1,0] A --> F[apple = 0,0,0,0,1] style B fill:#e74c3c style C fill:#e74c3c style D fill:#e74c3c style E fill:#e74c3c style F fill:#e74c3c Problem: No semantic relationship! ...