Interactive map of GPT-2's token embedding space
๐กVisualize the internal geometry of LLM embeddings to better understand how models represent linguistic relationships.
โก 30-Second TL;DR
What Changed
Visualizes 32,070 tokens from GPT-2-small's WTE (Word Token Embedding).
Why It Matters
Provides a tangible way for researchers to interpret high-dimensional embedding spaces. It helps in understanding how models cluster linguistic concepts without needing a forward pass.
What To Do Next
Visit the interactive map to visualize how your own model's embeddings cluster by performing a similar t-SNE projection on your weight matrices.
Key Points
- โขVisualizes 32,070 tokens from GPT-2-small's WTE (Word Token Embedding).
- โขUses t-SNE for dimensionality reduction and minimum spanning tree for edge connections.
- โขAllows users to click tokens to explore semantic relationships and nearest neighbors.
- โขFully mobile-responsive interface with search functionality.
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขThe visualization project was developed by researchers to demystify the 'black box' nature of transformer embedding layers by mapping high-dimensional vectors into a 2D manifold.
- โขThe underlying embedding matrix for GPT-2-small consists of 50,257 total tokens, though this specific visualization focuses on a subset of 32,070 alphabetic tokens to reduce visual noise.
- โขThe project utilizes the t-SNE (t-Distributed Stochastic Neighbor Embedding) algorithm with a perplexity setting optimized to balance local cluster density and global structure preservation.
- โขThe implementation relies on WebGL-based rendering to maintain high frame rates while interacting with thousands of nodes and edges in the browser.
- โขThe tool highlights the 'polysemy' problem in LLMs, where tokens with multiple meanings often appear in distinct clusters or bridge different semantic regions of the embedding space.
๐ ๏ธ Technical Deep Dive
- Model Architecture: GPT-2-small (12 layers, 768 hidden dimension, 12 attention heads).
- Embedding Dimension: 768-dimensional vectors projected to 2D using t-SNE.
- Graph Construction: Minimum Spanning Tree (MST) algorithm used to define connectivity between tokens based on cosine similarity.
- Data Processing: Pre-processing involved normalizing embedding vectors to unit length to ensure cosine similarity corresponds to Euclidean distance in the projected space.
- Frontend Stack: Built using D3.js for data manipulation and Canvas API for high-performance rendering of the token graph.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
โณ Timeline
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