Instruction-Tuned Language Models Cannot Sample from Distributions They Can Describe
Silicon sampling uses language models as proxies for human survey respondents, treating each model call as an independent draw from the persona's response distribution. We show this draw does not exist: instruction-tuned models do not sample from distributions, they collapse to a single output. The same persona on the same question returns the same answer on more than half of items in a public-opinion benchmark, and the model's internal probabilities concentrate on a single option. The failure is associated with, and amplified by, instruction-targeted post-training: instruction-tuned models are worse than their own bases in every family we can compare, the gap widens at each successive po...
| Authors | Chaemin Jang, Dongman Lee, Jihee Kim |
|---|---|
| Primary category | cs.AI |
| Published | 28 July 2026 |
| Last updated at source | 10 October 2026 |
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