{
  "id": "prompt-tuning",
  "code": "PTL-0076",
  "term": "Prompt Tuning",
  "aliases": [
    "soft prompts",
    "soft prompt tuning"
  ],
  "category": "optimization",
  "definition": "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.",
  "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.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "prefix-tuning",
    "low-rank-adaptation"
  ],
  "introduced": 2021,
  "sources": [
    {
      "title": "The Power of Scale for Parameter-Efficient Prompt Tuning",
      "authors": "Lester et al.",
      "year": 2021,
      "url": "https://arxiv.org/abs/2104.08691"
    }
  ],
  "url": "https://protologue.com/t/prompt-tuning/",
  "citation": "Protologue. (2026). Prompt Tuning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0076). https://protologue.com/t/prompt-tuning/"
}