Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always
Large language models (LLMs) have been proposed as alternatives to human experts for estimating unknown quantities with associated uncertainty, a process known as Bayesian elicitation. We test this by asking eleven LLMs to estimate population statistics, such as health prevalence rates, personality trait distributions, and labor market figures, and to express their uncertainty as 95\% credible intervals. We vary each model's reasoning effort (low, medium, high) to test whether more "thinking" improves results. Our findings reveal three key results. First, larger, more capable models produce more accurate estimates, but increasing reasoning effort provides no consistent benefit. Second, al...
| Authors | Luka Hobor, Mario Brcic, Mihael Kovac et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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