# Learning to Generate Task-Specific Adapters from Task Description

Authors: Qinyuan Ye, Xiang Ren.

Introduces Hypter, which generates adapters from natural-language task descriptions.

ArXiv initial submission: 2021-01-02.

[Original paper](https://arxiv.org/abs/2101.00420) · [Reading-list entry](https://programasweights.com/reading-list/papers/2101.00420/)
[Preprint BibTeX](https://programasweights.com/reading-list/papers/2101.00420/citation.bib) · [Preprint CSL-JSON](https://programasweights.com/reading-list/papers/2101.00420/citation.csl.json) · [JSON record](https://programasweights.com/reading-list/papers/2101.00420/record.json)

**Contribution:** Combines a description encoder and adapter generator with BART. Tests generalization to unseen tasks on ZEST and synthetic SQuAD.

**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 to PAW:** An early description-to-adapter method cited by PAW. PAW develops the programming interface for saving and calling the resulting neural functions.

**Limitations:** Training requires a collection of tasks with descriptions and labeled examples.

[Discussion in the paper](https://arxiv.org/html/2607.02512v1#S10)

Related terms: Hypter, task descriptions, generated adapters, zero-shot learning.

## Method comparisons

**From generated adapters to reusable functions**

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.

Sources: [PAW related work](https://arxiv.org/html/2607.02512v1#S10) · [Text-to-LoRA paper](https://proceedings.mlr.press/v267/charakorn25a.html)

Paper details: [Learning to Generate Task-Specific Adapters from Task Description](https://programasweights.com/reading-list/papers/2101.00420/) · [HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation](https://programasweights.com/reading-list/papers/2212.10315/) · [HyperTuning: Toward Adapting Large Language Models without Back-propagation](https://programasweights.com/reading-list/papers/2211.12485/) · [Text-to-LoRA: Instant Transformer Adaption](https://programasweights.com/reading-list/papers/2506.06105/) · [Program-as-Weights: A Programming Paradigm for Fuzzy Functions](https://programasweights.com/reading-list/papers/2607.02512/)


## Published version

Learning to Generate Task-Specific Adapters from Task Description. Qinyuan Ye, Xiang Ren. 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), 2021.
[Publication](https://aclanthology.org/2021.acl-short.82/) · [BibTeX](https://programasweights.com/reading-list/papers/2101.00420/publication.bib) · [CSL-JSON](https://programasweights.com/reading-list/papers/2101.00420/publication.csl.json)
[Publication record](https://aclanthology.org/2021.acl-short.82/).

## Approaches

- [Generated weights and adapters](https://programasweights.com/reading-list/guides/compilation/#generated-adapters): Generate model weights from a task description, examples, or context.

Reading-list version 1.2.0; updated 2026-10-19.