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

Jul 15, 2026

A new preprint introduces the One-Word Census, a minimal and low-cost method to measure answer-choice conformity across 44 language models. The study finds that when prompted to 'pick a word,' 41% of models chose 'serendipity,' and in several categories, a single answer dominated responses. The research reveals that newer flagship models tend to be the most conformist, while persona- and community-tuned models are more divergent. The findings highlight structured variation in conformity across model families and generations.

Why it matters: This work provides a practical tool for benchmarking diversity and conformity in language model outputs, with implications for understanding creativity and bias in AI systems.

Full story at: arXiv Computation and Language ↗