Prefix Tuning
Prefix tuning learns continuous task-specific vectors that are prepended to the activations at every layer of a frozen language model, steering generation without changing the model's weights.
Description
It was an early parameter-efficient alternative to fine-tuning for generation tasks such as table-to-text and summarization.
Sources
- Li & Liang (2021). Prefix-Tuning: Optimizing Continuous Prompts for Generation.
Cite this entry
Protologue. (2026). Prefix Tuning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0077). https://protologue.com/t/prefix-tuning/
BibTeX
@misc{protologue_prefix_tuning,
title = {Prefix Tuning},
author = {{Protologue}},
year = {2026},
howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.0.0},
note = {Entry PTL-0077},
url = {https://protologue.com/t/prefix-tuning/}
}