# Many-shot In-Context Learning

> Many-shot in-context learning places hundreds or thousands of demonstrations in a long-context prompt, often yielding large gains over few-shot prompting.

- Identifier: PTL-0022
- Category: Exemplars & In-Context Learning
- Canonical URL: https://protologue.com/t/many-shot-in-context-learning/
- Also known as: many-shot ICL, long-context ICL
- Introduced: 2024

## Description

Agarwal et al. found consistent improvements as the number of shots grew into the hundreds, and introduced variants that use model-generated rationales or unlabeled problems. The same scaling behavior underlies the many-shot jailbreaking attack.

## Broader terms

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

## Related terms

- [Few-shot Prompting](https://protologue.com/t/few-shot-prompting/)
- [Many-shot Jailbreaking](https://protologue.com/t/many-shot-jailbreaking/)
- [Context Window](https://protologue.com/t/context-window/)

## Sources

- Agarwal et al. (2024). Many-Shot In-Context Learning. https://arxiv.org/abs/2404.11018

## Cite this entry

Protologue. (2026). Many-shot In-Context Learning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0022). https://protologue.com/t/many-shot-in-context-learning/

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