{
  "id": "2101.00190",
  "title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation",
  "authors": "Xiang Lisa Li, Percy Liang",
  "first_public": "2021-01-01",
  "first_public_source": "https://arxiv.org/abs/2101.00190",
  "arxiv_first_submission": "2021-01-01",
  "date_label": "arXiv v1",
  "arxiv": "2101.00190",
  "paper_url": "https://arxiv.org/abs/2101.00190",
  "venue": "ACL-IJCNLP 2021",
  "lens": [
    "peft"
  ],
  "related_terms": [
    "prefix-tuning",
    "continuous prompts",
    "virtual tokens",
    "parameter-efficient fine-tuning"
  ],
  "summary": "Adapts a frozen language model by learning a short sequence of continuous prefix vectors.",
  "contribution": "Trains prefixes for table-to-text generation and summarization while updating far fewer parameters than full fine-tuning.",
  "limitations": "A separate prefix is optimized for each task.",
  "program_form": "Continuous prefix activations.",
  "conditioning": "Training examples for the target task.",
  "execution": "The frozen language model attends to the learned prefix.",
  "relationship": "PAW evaluates compiler-generated prefixes alongside LoRA adapters.",
  "relationship_source": "https://arxiv.org/html/2607.02512v1#S10",
  "provenance": "arXiv record and paper.",
  "author_names": [
    "Xiang Lisa Li",
    "Percy Liang"
  ],
  "record_url": "https://programasweights.com/reading-list/papers/2101.00190/",
  "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2101-00190",
  "citation_date": "2021-01-01",
  "json_url": "https://programasweights.com/reading-list/papers/2101.00190/record.json",
  "markdown_url": "https://programasweights.com/reading-list/papers/2101.00190/index.md",
  "bibtex_url": "https://programasweights.com/reading-list/papers/2101.00190/citation.bib",
  "csl_url": "https://programasweights.com/reading-list/papers/2101.00190/citation.csl.json",
  "publication": {
    "title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation",
    "author_names": [
      "Xiang Lisa Li",
      "Percy Liang"
    ],
    "year": 2021,
    "type": "paper-conference",
    "container_title": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    "url": "https://aclanthology.org/2021.acl-long.353/",
    "source_url": "https://aclanthology.org/2021.acl-long.353/",
    "verified_at": "2026-10-19",
    "doi": "10.18653/v1/2021.acl-long.353",
    "publisher": "Association for Computational Linguistics",
    "pages": "4582-4597"
  },
  "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2101.00190/publication.bib",
  "publication_csl_url": "https://programasweights.com/reading-list/papers/2101.00190/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"
    }
  ]
}