{
  "id": "2211.12485",
  "title": "HyperTuning: Toward Adapting Large Language Models without Back-propagation",
  "authors": "Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen",
  "first_public": "2022-11-22",
  "first_public_source": "https://arxiv.org/abs/2211.12485",
  "arxiv_first_submission": "2022-11-22",
  "date_label": "arXiv v1",
  "arxiv": "2211.12485",
  "paper_url": "https://arxiv.org/abs/2211.12485",
  "venue": "ICML 2023",
  "lens": [
    "generated-adapters"
  ],
  "related_terms": [
    "HyperTuning",
    "HyperT5",
    "few-shot adaptation",
    "generated LoRA"
  ],
  "summary": "Produces task-specific parameters for a frozen language model from a few demonstrations.",
  "contribution": "Introduces HyperT5 models that generate prefixes or LoRA parameters. Hyperpretraining and multitask training prepare the generator for new tasks.",
  "limitations": "The hypermodel accepts a limited number of demonstrations. The paper also studies further task-specific tuning.",
  "program_form": "Generated prefix or LoRA parameters.",
  "conditioning": "A few examples of the task.",
  "execution": "A frozen T5 model runs with the generated parameters.",
  "relationship": "PAW cites HyperTuning as prior work on generating adaptation parameters from examples.",
  "relationship_source": "https://arxiv.org/html/2607.02512v1#S10",
  "provenance": "arXiv record and paper.",
  "author_names": [
    "Jason Phang",
    "Yi Mao",
    "Pengcheng He",
    "Weizhu Chen"
  ],
  "record_url": "https://programasweights.com/reading-list/papers/2211.12485/",
  "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2211-12485",
  "citation_date": "2022-11-22",
  "json_url": "https://programasweights.com/reading-list/papers/2211.12485/record.json",
  "markdown_url": "https://programasweights.com/reading-list/papers/2211.12485/index.md",
  "bibtex_url": "https://programasweights.com/reading-list/papers/2211.12485/citation.bib",
  "csl_url": "https://programasweights.com/reading-list/papers/2211.12485/citation.csl.json",
  "publication": {
    "title": "HyperTuning: Toward Adapting Large Language Models without Back-propagation",
    "author_names": [
      "Jason Phang",
      "Yi Mao",
      "Pengcheng He",
      "Weizhu Chen"
    ],
    "year": 2023,
    "type": "paper-conference",
    "container_title": "Proceedings of the 40th International Conference on Machine Learning",
    "url": "https://proceedings.mlr.press/v202/phang23a.html",
    "source_url": "https://proceedings.mlr.press/v202/phang23a.html",
    "verified_at": "2026-10-19",
    "volume": "202",
    "publisher": "PMLR",
    "pages": "27854-27875"
  },
  "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2211.12485/publication.bib",
  "publication_csl_url": "https://programasweights.com/reading-list/papers/2211.12485/publication.csl.json",
  "method_comparisons": [
    {
      "title": "From generated adapters to reusable functions",
      "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.",
      "source_ids": [
        "2101.00420",
        "2212.10315",
        "2211.12485",
        "2506.06105",
        "2607.02512"
      ],
      "source_urls": [
        "https://arxiv.org/html/2607.02512v1#S10",
        "https://proceedings.mlr.press/v267/charakorn25a.html"
      ],
      "source_labels": [
        "PAW related work",
        "Text-to-LoRA paper"
      ],
      "topic_id": "generated-adapters",
      "topic_label": "Generated weights and adapters",
      "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters"
    }
  ]
}