# In-Context Learning

> In-context learning (ICL) is a language model's ability to perform a task by conditioning on instructions or demonstrations in its prompt, without any update to its weights.

- Identifier: PTL-0017
- Category: Exemplars & In-Context Learning
- Canonical URL: https://protologue.com/t/in-context-learning/
- Also known as: ICL
- Introduced: 2020

## Description

Identified as an emergent capability of large pretrained models in the GPT-3 paper, ICL underlies few-shot prompting. Studies show that models often rely more on the format and label space of demonstrations than on whether the demonstration labels are correct.

## Narrower terms

- [Few-shot Prompting](https://protologue.com/t/few-shot-prompting/)
- [Demonstration Label Sensitivity](https://protologue.com/t/demonstration-label-sensitivity/)
- [Many-shot In-Context Learning](https://protologue.com/t/many-shot-in-context-learning/)

## Sources

- Brown et al. (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165

## Cite this entry

Protologue. (2026). In-Context Learning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0017). https://protologue.com/t/in-context-learning/

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