Researchers explored the embedding geometry of the token "Trump" within the GPT-2 Small model's static embedding table. By analyzing nearest neighbors under both discretized and continuous representations of the token's embedding, they observed distinct outcomes. The discretized approach yielded generic political terms, while the continuous approach revealed a more specific set of related individuals, including family members, staff, and other presidents. AI
IMPACT Provides insight into how specific tokens are represented within a language model's embedding space.
RANK_REASON Analysis of model embeddings and token geometry. [lever_c_demoted from research: ic=1 ai=1.0]
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