# Temperature

> Temperature is a sampling parameter that rescales a model's output probabilities before a token is chosen; lower values make outputs more deterministic and higher values make them more varied.

- Identifier: PTL-0006
- Category: Foundations
- Canonical URL: https://protologue.com/t/temperature/
- Also known as: sampling temperature

## Description

At temperature zero the model approximately always picks its most likely token (greedy decoding). Techniques that rely on diverse samples, such as self-consistency and best-of-N, deliberately use a nonzero temperature, while extraction and classification tasks usually use a low one.

## Related terms

- [Top-p Sampling](https://protologue.com/t/top-p-sampling/)
- [Self-Consistency](https://protologue.com/t/self-consistency/)
- [Best-of-N Sampling](https://protologue.com/t/best-of-n-sampling/)

## Sources

- Holtzman et al. (2019). The Curious Case of Neural Text Degeneration. https://arxiv.org/abs/1904.09751

## Cite this entry

Protologue. (2026). Temperature. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0006). https://protologue.com/t/temperature/

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