{
  "id": "2101.00420",
  "title": "Learning to Generate Task-Specific Adapters from Task Description",
  "authors": "Qinyuan Ye, Xiang Ren",
  "first_public": "2021-01-02",
  "first_public_source": "https://arxiv.org/abs/2101.00420",
  "arxiv_first_submission": "2021-01-02",
  "date_label": "arXiv v1",
  "arxiv": "2101.00420",
  "paper_url": "https://arxiv.org/abs/2101.00420",
  "venue": "ACL-IJCNLP 2021",
  "lens": [
    "generated-adapters"
  ],
  "related_terms": [
    "Hypter",
    "task descriptions",
    "generated adapters",
    "zero-shot learning"
  ],
  "summary": "Introduces Hypter, which generates adapters from natural-language task descriptions.",
  "contribution": "Combines a description encoder and adapter generator with BART. Tests generalization to unseen tasks on ZEST and synthetic SQuAD.",
  "limitations": "Training requires a collection of tasks with descriptions and labeled examples.",
  "program_form": "Bottleneck adapters inserted into a text-to-text model.",
  "conditioning": "A natural-language task description.",
  "execution": "BART performs the task with the generated adapters.",
  "relationship": "An early description-to-adapter method cited by PAW. PAW develops the programming interface for saving and calling the resulting neural functions.",
  "relationship_source": "https://arxiv.org/html/2607.02512v1#S10",
  "provenance": "arXiv record and paper.",
  "author_names": [
    "Qinyuan Ye",
    "Xiang Ren"
  ],
  "record_url": "https://programasweights.com/reading-list/papers/2101.00420/",
  "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2101-00420",
  "citation_date": "2021-01-02",
  "json_url": "https://programasweights.com/reading-list/papers/2101.00420/record.json",
  "markdown_url": "https://programasweights.com/reading-list/papers/2101.00420/index.md",
  "bibtex_url": "https://programasweights.com/reading-list/papers/2101.00420/citation.bib",
  "csl_url": "https://programasweights.com/reading-list/papers/2101.00420/citation.csl.json",
  "publication": {
    "title": "Learning to Generate Task-Specific Adapters from Task Description",
    "author_names": [
      "Qinyuan Ye",
      "Xiang Ren"
    ],
    "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 2: Short Papers)",
    "url": "https://aclanthology.org/2021.acl-short.82/",
    "source_url": "https://aclanthology.org/2021.acl-short.82/",
    "verified_at": "2026-10-19",
    "doi": "10.18653/v1/2021.acl-short.82",
    "publisher": "Association for Computational Linguistics",
    "pages": "646-653"
  },
  "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2101.00420/publication.bib",
  "publication_csl_url": "https://programasweights.com/reading-list/papers/2101.00420/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"
    }
  ]
}