# Few-shot Calibration

> Few-shot calibration corrects a model's systematic biases toward particular answers, such as the most frequent or most recent label in the examples, by adjusting output probabilities measured on a content-free input.

- Identifier: PTL-0021
- Category: Exemplars & In-Context Learning
- Canonical URL: https://protologue.com/t/few-shot-calibration/
- Also known as: contextual calibration, calibrate before use

## Description

Zhao et al. identified majority-label bias, recency bias, and common-token bias in few-shot prompting, and proposed contextual calibration, which estimates the bias from an input such as "N/A" and rescales predictions to neutralize it.

## Broader terms

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

## Related terms

- [Exemplar Ordering](https://protologue.com/t/exemplar-ordering/)
- [Prompt Sensitivity](https://protologue.com/t/prompt-sensitivity/)

## Sources

- Zhao et al. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models. https://arxiv.org/abs/2102.09690

## Cite this entry

Protologue. (2026). Few-shot Calibration. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0021). https://protologue.com/t/few-shot-calibration/

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