GPT-2's embedding reveals political networks via token proximity

The analysis examined a single token, "Trump," in GPT-2 Small's static embedding table—the lookup table of learned numerical vectors assigned before attention or context is applied. The researcher created a t-SNE projection (a dimensionality-reduction technique for visualization) of 32,070 alphabetic tokens with at least two characters to show the overall embedding landscape. Two nearest-neighbor analyses of Trump's embedding then revealed how representation choice shapes which tokens emerge as most similar. Under the discretized representation, where each coordinate is thresholded, the nearest neighbors are mostly generic political figures: Mitt, Hillary, Pelosi, and Blair. Under the continuous representation, which preserves the original embedding coordinates, the neighbors form a more specific and coherent cluster: family members (such as Ivanka and Melania, implied by the category "family members"), staff, rivals, and other U.S. presidents including Obama, Clinton, Bush, and Eisenhower. Crucially, this entire analysis operates on the model's learned embeddings without prompting, text generation, or any intervention beyond mathematical transformation of the vector space. The difference between the two representations demonstrates that GPT-2 Small has encoded layered semantic structure—both generic political categorization and specific social relationships—within the geometry of its token embeddings.

The visualization leverages a fundamental property of neural language models: static token embeddings—the learned numerical representations assigned to each word or subword before any context-dependent processing—encode rich semantic structure. By comparing nearest neighbors under two different mathematical operations on the same embedding, the analysis isolates how the model's geometry alone organizes political and social knowledge. The continuous representation preserves the fine-grained distinctions the model has learned (distinguishing Obama or Clinton from generic politicians like Mitt or Pelosi), while the discretized version collapses those distinctions and reveals a coarser clustering. This suggests the model has distributed specific relationship information—family ties, professional roles, rivalries—across multiple coordinate dimensions, and that this information is recoverable without any text generation or prompt engineering. The finding is grounded purely in what GPT-2 Small has already learned during pretraining on its training corpus.

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