{
  "id": "prefix-tuning",
  "code": "PTL-0077",
  "term": "Prefix Tuning",
  "aliases": [],
  "category": "optimization",
  "definition": "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.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "prompt-tuning",
    "low-rank-adaptation"
  ],
  "introduced": 2021,
  "sources": [
    {
      "title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation",
      "authors": "Li & Liang",
      "year": 2021,
      "url": "https://arxiv.org/abs/2101.00190"
    }
  ],
  "url": "https://protologue.com/t/prefix-tuning/",
  "citation": "Protologue. (2026). Prefix Tuning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0077). https://protologue.com/t/prefix-tuning/"
}