{
  "id": "2212.10315",
  "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation",
  "authors": "Hamish Ivison, Akshita Bhagia, Yizhong Wang, Hannaneh Hajishirzi, Matthew Peters",
  "first_public": "2022-12-20",
  "first_public_source": "https://arxiv.org/abs/2212.10315",
  "arxiv_first_submission": "2022-12-20",
  "date_label": "arXiv v1",
  "arxiv": "2212.10315",
  "paper_url": "https://arxiv.org/abs/2212.10315",
  "venue": "ACL 2023",
  "lens": [
    "generated-adapters"
  ],
  "related_terms": [
    "HINT",
    "instruction tuning",
    "hypernetwork",
    "instruction fusion"
  ],
  "summary": "Turns instructions and examples into reusable prefixes, adapters, and encoded instruction representations.",
  "contribution": "Combines a hypernetwork with instruction fusion so repeated calls can reuse the instruction computation. Evaluates zero-shot and few-shot generalization.",
  "limitations": "The generated modules and cached instruction representations are tied to the trained encoder-decoder model.",
  "program_form": "Prefixes, adapters, and encoded instruction representations.",
  "conditioning": "Task instructions and optional examples.",
  "execution": "An encoder-decoder model uses the generated modules and attends to cached instruction representations.",
  "relationship": "HINT reuses generated modules and an encoded instruction. PAW reuses a program containing text and adaptation parameters.",
  "relationship_source": "https://arxiv.org/html/2607.02512v1#S10",
  "provenance": "arXiv record and paper.",
  "author_names": [
    "Hamish Ivison",
    "Akshita Bhagia",
    "Yizhong Wang",
    "Hannaneh Hajishirzi",
    "Matthew Peters"
  ],
  "record_url": "https://programasweights.com/reading-list/papers/2212.10315/",
  "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2212-10315",
  "citation_date": "2022-12-20",
  "json_url": "https://programasweights.com/reading-list/papers/2212.10315/record.json",
  "markdown_url": "https://programasweights.com/reading-list/papers/2212.10315/index.md",
  "bibtex_url": "https://programasweights.com/reading-list/papers/2212.10315/citation.bib",
  "csl_url": "https://programasweights.com/reading-list/papers/2212.10315/citation.csl.json",
  "publication": {
    "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation",
    "author_names": [
      "Hamish Ivison",
      "Akshita Bhagia",
      "Yizhong Wang",
      "Hannaneh Hajishirzi",
      "Matthew E. Peters"
    ],
    "year": 2023,
    "type": "paper-conference",
    "container_title": "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    "url": "https://aclanthology.org/2023.acl-long.631/",
    "source_url": "https://aclanthology.org/2023.acl-long.631/",
    "verified_at": "2026-10-19",
    "doi": "10.18653/v1/2023.acl-long.631",
    "publisher": "Association for Computational Linguistics",
    "pages": "11272-11288"
  },
  "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2212.10315/publication.bib",
  "publication_csl_url": "https://programasweights.com/reading-list/papers/2212.10315/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"
    }
  ]
}