---
title: "UniPELT — paper by Mao et al. (2022) — summary and ideas"
description: "Parameter-efficient language model tuning (PELT) methods adapt a frozen pre-trained… 0.89 points. 26 references, cited by 76 papers here."
authors: ["Yuning Mao", "Lambert Mathias", "Rui Hou", "Amjad Almahairi", "Hao Ma", "Jiawei Han", "Scott Yih", "Madian Khabsa"]
year: 2022
venue: "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2022.acl-long.433/"
doi: "10.18653/v1/2022.acl-long.433"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mao-2022-unipelt-unified-framework-parameter-efficient/"
---
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# UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning

**Authors:** [Yuning Mao](https://md.research.elamur.ai/free/en/authors/yuning-mao/), Lambert Mathias, [Rui Hou](https://md.research.elamur.ai/free/en/authors/rui-hou/), [Amjad Almahairi](https://md.research.elamur.ai/free/en/authors/amjad-almahairi/), Hao Ma, [Jiawei Han](https://md.research.elamur.ai/free/en/authors/jiawei-han/), [Scott Yih](https://md.research.elamur.ai/free/en/authors/scott-yih/) and [Madian Khabsa](https://md.research.elamur.ai/free/en/authors/madian-khabsa/) · **Year:** 2022 · **Venue:** [Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2022](https://md.research.elamur.ai/free/en/venues/acl/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.acl-long.433/) · [PDF](https://aclanthology.org/2022.acl-long.433.pdf) · [DOI 10.18653/v1/2022.acl-long.433](https://doi.org/10.18653/v1/2022.acl-long.433) · [OpenAlex](https://openalex.org/W3206816211)

**Citations:** 86 citations (OpenAlex, 2026-10-14) · [76](https://md.research.elamur.ai/free/en/papers/mao-2022-unipelt-unified-framework-parameter-efficient/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Parameter-efficient language model tuning (PELT) methods adapt a frozen pre-trained model by training only a tiny set of parameters. Their effectiveness, however, differs a lot from task to task, so practitioners must test many options to find the right one. UniPELT removes this selection step. It places several PELT techniques inside one model: adapter after the feedforward block, prefix-tuning before multi-head attention, and LoRA on the query/key projections. Each technique receives a learned gate that scales its contribution up or down for the current task or input. Because the techniques modify different parts of the Transformer, they can coexist without directly interfering. The authors evaluated the framework on GLUE across eight tasks and several training-set sizes. UniPELT reliably beat its strongest individual component and also exceeded full fine-tuning. It often did better than an oracle that picks the best single method per task, which suggests that mixing methods has value of its own. The cost stays small, at roughly 1% trainable parameters and modest inference overhead. This makes UniPELT a robust default when choosing among PELT approaches.

## Key points
- The study examines four representative PELT methods: adapter, prefix-tuning, LoRA and BitFit. BitFit was left out of UniPELT because it performed worst in preliminary tests.
- The experiments cover GLUE with 32 setups (8 tasks × 4 data sizes) and more than 1,000 runs. UniPELT gains about 1–4 points over the best submodule it contains.
- With full training data, UniPELT (APL) reaches a GLUE dev average of 83.50, compared with 82.67 for fine-tuning. A variant without gating falls 0.89 points behind its gated counterpart, which shows the gates matter.
- Relative to a 110M-parameter model, UniPELT trains only 0.99–1.26% of the parameters. It trains 30–50% faster than fine-tuning, and its inference is about 4–11% slower than its slowest submodule.
- Code is released at github.com/morningmoni/UniPELT (ACL 2022, UIUC and Meta AI).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 108 works · 67,432 citations

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## Cite

Cite as: Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Scott Yih and Madian Khabsa. 2022. UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mao2022unipelt,
  title = {{UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning}},
  author = {Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Scott Yih and Madian Khabsa},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.acl-long.433},
  url = {https://doi.org/10.18653/v1/2022.acl-long.433}
}
```

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## References

26 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*