# Demonstration Label Sensitivity

> Demonstration label sensitivity refers to how much a model's few-shot performance depends on whether the example labels are correct; research found that randomly replacing labels often hurts performance only slightly.

- Identifier: PTL-0018
- Category: Exemplars & In-Context Learning
- Canonical URL: https://protologue.com/t/demonstration-label-sensitivity/
- Also known as: role of demonstrations

## Description

Min et al. showed that demonstrations mainly supply the label space, the input distribution, and the format of the task. This suggests few-shot examples work largely by specifying what the task looks like rather than by teaching the input-label mapping.

## Broader terms

- [In-Context Learning](https://protologue.com/t/in-context-learning/)

## Related terms

- [Few-shot Prompting](https://protologue.com/t/few-shot-prompting/)
- [Exemplar Selection](https://protologue.com/t/exemplar-selection/)

## Sources

- Min et al. (2022). Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?. https://arxiv.org/abs/2202.12837

## Cite this entry

Protologue. (2026). Demonstration Label Sensitivity. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0018). https://protologue.com/t/demonstration-label-sensitivity/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)