# Prompt Tuning

> Prompt tuning learns a small set of continuous "soft prompt" embeddings that are prepended to the input, by gradient descent, while keeping the language model's weights frozen.

- Identifier: PTL-0076
- Category: Prompt Optimization
- Canonical URL: https://protologue.com/t/prompt-tuning/
- Also known as: soft prompts, soft prompt tuning
- Introduced: 2021

## Description

Lester et al. showed that as models grow, prompt tuning approaches the quality of full fine-tuning while storing only a tiny number of task-specific parameters. Soft prompts are vectors rather than readable text.

## Related terms

- [Prefix Tuning](https://protologue.com/t/prefix-tuning/)
- [Low-Rank Adaptation](https://protologue.com/t/low-rank-adaptation/)

## Sources

- Lester et al. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. https://arxiv.org/abs/2104.08691

## Cite this entry

Protologue. (2026). Prompt Tuning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0076). https://protologue.com/t/prompt-tuning/

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