ModelRefs / Active Prompting — AI Glossary
Active Prompting — AI Glossary
Selecting the most informative few-shot examples to annotate by measuring model uncertainty, then using human-annotated examples.
Overview
Diao et al. (2023) proposed selecting CoT few-shot examples by measuring disagreement across multiple model samples (high uncertainty = high informativeness), then having humans annotate those examples. Outperforms random few-shot selection across multiple reasoning benchmarks with the same annotation budget.
Reference details
| Topic | prompting |
|---|---|
| Last reviewed | 2026-06-24 |
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Frequently asked questions
What is Active Prompting?
Selecting the most informative few-shot examples to annotate by measuring model uncertainty, then using human-annotated examples.
What concepts are related to Active Prompting?
Closely related concepts include few shot, chain of thought, calibration.