Visualizing GPT-2 Embedding Geometry for Token 'Trump'
💡Learn how raw embedding geometry shapes model associations and how coordinate representation alters semantic output.
⚡ 30-Second TL;DR
What Changed
Analyzed GPT-2 Small static embeddings for the token 'Trump' without context or attention.
Why It Matters
Understanding embedding geometry helps practitioners interpret model biases and semantic relationships. It highlights that raw embedding tables contain significant structural information before any transformer layers are applied.
What To Do Next
Use t-SNE or UMAP to visualize your model's static embedding table to identify potential semantic biases or clustering issues before training.
Key Points
- •Analyzed GPT-2 Small static embeddings for the token 'Trump' without context or attention.
- •Discretized representation leads to generic political terms like 'Hillary' and 'Pelosi'.
- •Continuous representation captures more specific associations including family, staff, and rivals.
- •Demonstrates that embedding geometry is highly sensitive to coordinate processing methods.
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Original source: Reddit r/MachineLearning ↗
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