The One-Word Census: Answer-Choice Conformity Across 105 Language Models
When a language model must choose one answer from a large space of equally valid options, which answer does it choose, and how often is it the answer every other model chooses? Asked to "pick a word," 105 language models from more than twenty labs chose serendipity 46% of the time. We measure this convergence, and each model's share in it, with 96 single-turn prompts that each name a category with many valid one-word answers ("Name a tree."), asked eight times per model and scored by exact match, with no embeddings and no judge. A model's answer-choice surprisal is the average -log2 probability of its answers under the pooled answers of all other models. In 28 of 96 categories a single an...
| Authors | Tapan Parikh |
|---|---|
| Primary category | cs.CL |
| Published | 14 July 2026 |
| Last updated at source | 9 October 2026 |
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