{
  "id": "low-rank-adaptation",
  "code": "PTL-0078",
  "term": "Low-Rank Adaptation",
  "aliases": [
    "LoRA"
  ],
  "category": "optimization",
  "definition": "Low-rank adaptation (LoRA) fine-tunes a language model by training small low-rank matrices added to its weight layers while freezing the original weights, drastically reducing the number of trainable parameters.",
  "description": "LoRA is a common alternative when prompting alone cannot reach the required behavior, and adapters can be swapped per task on one base model.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "prompt-tuning",
    "prefix-tuning",
    "instruction-tuning"
  ],
  "introduced": 2021,
  "sources": [
    {
      "title": "LoRA: Low-Rank Adaptation of Large Language Models",
      "authors": "Hu et al.",
      "year": 2021,
      "url": "https://arxiv.org/abs/2106.09685"
    }
  ],
  "url": "https://protologue.com/t/low-rank-adaptation/",
  "citation": "Protologue. (2026). Low-Rank Adaptation. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0078). https://protologue.com/t/low-rank-adaptation/"
}