The One-Word Census: Answer-Choice Conformity Across 44 Language Models

[Submitted on 14 Jul 2026]

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Abstract: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 is well documented; our contribution is the instrument itself — the One-Word Census — and what it reveals about the structure of the convergence. We score each model by answer-choice surprisal: the average $-\log2$ probability of its answers under the pooled answers of all other models, leave-one-out. Convergence is extreme — in 7 of 31 categories one answer takes over 80% of all answers — yet conformity varies more than fourfold across models, and the variation is structured. Persona- and community-tuned models are the most divergent; the newest mainline flagships are the most conformist, producing almost no answer no other model gave. Within four lineages (Claude, GPT, Qwen, Grok) conformity rises with each generation — but reverses for the latest flagship Claude and GPT models, a possible early signal of repositioning at the top tier. Rankings are robust to roster composition (leave-one-family-out rho = 0.985). Against human category-production norms, the field is more concentrated than people in 18 of 20 shared categories. All prompts, transcripts, and code are public.

Submission history

From: Tapan Parikh [view email]
[v1] Tue, 14 Jul 2026 14:12:05 UTC (293 KB)