The One-Word Census: Answer-Choice Conformity Across 44 Language Models
When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks? Asked to "pick a word -- any word," 44 models chose "serendipity" 41% of the time. We characterize this convergence with a deliberately minimal instrument: 31 single-turn prompts, each naming a category with many valid one-word answers ("Name a tree."), asked four times per model with no system prompt. Analysis is exact-match on normalized tokens -- no embeddings, no judge -- at about a dollar per model. That models converge
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- PossiblePossibly related (embedding) · 49%Knowledge Distillation of Black-Box Large Language Models →
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- PossiblePossibly related (embedding) · 48%Jeryi-Sun/LLM-and-Law →
- PossiblePossibly related (embedding) · 48%chrisliu298/awesome-llm-unlearning →
- LinkedLinked via arxiv author · 85%Tapan Parikh →
“The One-Word Census: Answer-Choice Conformity Across 44 Language Models”