GPT-2 Small’s embedding geometry around “Trump”: discretized vs. continuous nearest neighbours [P]

This visualization looks at the token “Trump” in GPT-2 Small’s static embedding table, before attention or context is applied.

The top plot is a t-SNE projection of 32,070 alphabetic tokens with at least two characters.

The two graphs below compare Trump’s nearest neighbours under two representations of the same embedding:

Discretized: each coordinate is thresholded before neighbours are calculated. This produces mostly generic political terms such as Mitt, Hillary, Pelosi, and Blair.

Continuous: the original coordinates are retained. This produces a more specific group containing family members, staff, rivals, and presidents including Obama, Clinton, Bush, and Eisenhower.

No prompting or text generation is involved; everything comes directly from GPT-2 Small’s learned token embeddings.