{
  "id": "2106.09685",
  "title": "LoRA: Low-Rank Adaptation of Large Language Models",
  "authors": "Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen",
  "first_public": "2021-06-17",
  "first_public_source": "https://arxiv.org/abs/2106.09685",
  "arxiv_first_submission": "2021-06-17",
  "date_label": "arXiv v1",
  "arxiv": "2106.09685",
  "paper_url": "https://arxiv.org/abs/2106.09685",
  "venue": "ICLR 2022",
  "lens": [
    "peft"
  ],
  "related_terms": [
    "LoRA",
    "low-rank adaptation",
    "parameter-efficient fine-tuning",
    "adapters"
  ],
  "summary": "Adapts a pretrained model with low-rank weight updates while keeping the original weights fixed.",
  "contribution": "Reduces the parameters trained and stored for each task. Evaluates adaptation across several language-model families.",
  "limitations": "A separate adapter is trained for each task.",
  "program_form": "Low-rank matrices that update selected model weights.",
  "conditioning": "Training examples for the target task.",
  "execution": "The language model applies the low-rank updates during inference.",
  "relationship": "PAW uses LoRA as a program representation and generates the adapter with a trained compiler.",
  "relationship_source": "https://arxiv.org/html/2607.02512v1#S10",
  "provenance": "arXiv record and paper.",
  "author_names": [
    "Edward J. Hu",
    "Yelong Shen",
    "Phillip Wallis",
    "Zeyuan Allen-Zhu",
    "Yuanzhi Li",
    "Shean Wang",
    "Lu Wang",
    "Weizhu Chen"
  ],
  "record_url": "https://programasweights.com/reading-list/papers/2106.09685/",
  "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2106-09685",
  "citation_date": "2021-06-17",
  "json_url": "https://programasweights.com/reading-list/papers/2106.09685/record.json",
  "markdown_url": "https://programasweights.com/reading-list/papers/2106.09685/index.md",
  "bibtex_url": "https://programasweights.com/reading-list/papers/2106.09685/citation.bib",
  "csl_url": "https://programasweights.com/reading-list/papers/2106.09685/citation.csl.json",
  "publication": {
    "title": "LoRA: Low-Rank Adaptation of Large Language Models",
    "author_names": [
      "Edward Hu",
      "Yelong Shen",
      "Phillip Wallis",
      "Zeyuan Allen-Zhu",
      "Yuanzhi Li",
      "Shean Wang",
      "Lu Wang",
      "Weizhu Chen"
    ],
    "year": 2022,
    "type": "paper-conference",
    "container_title": "International Conference on Learning Representations",
    "url": "https://openreview.net/forum?id=nZeVKeeFYf9",
    "source_url": "https://openreview.net/pdf?id=nZeVKeeFYf9",
    "verified_at": "2026-10-19"
  },
  "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2106.09685/publication.bib",
  "publication_csl_url": "https://programasweights.com/reading-list/papers/2106.09685/publication.csl.json",
  "method_comparisons": [
    {
      "title": "Program form and compiler",
      "text": "LoRA represents adaptation as low-rank weight updates; prefix tuning uses continuous activations. Their original methods learn these parameters from examples. PAW generates both forms from specifications.",
      "source_ids": [
        "2106.09685",
        "2101.00190",
        "2607.02512"
      ],
      "source_urls": [
        "https://arxiv.org/abs/2106.09685",
        "https://aclanthology.org/2021.acl-long.353/",
        "https://arxiv.org/html/2607.02512v1#S3"
      ],
      "source_labels": [
        "LoRA paper",
        "Prefix-tuning paper",
        "PAW implementations"
      ],
      "topic_id": "peft",
      "topic_label": "Parameter-efficient building blocks",
      "topic_url": "https://programasweights.com/reading-list/guides/compilation/#peft"
    }
  ]
}