@inproceedings{poth-etal-2023-adapters,
title = "Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning",
author = {Poth, Clifton and
Sterz, Hannah and
Paul, Indraneil and
Purkayastha, Sukannya and
Engl{\"a}nder, Leon and
Imhof, Timo and
Vuli{\'c}, Ivan and
Ruder, Sebastian and
Gurevych, Iryna and
Pfeiffer, Jonas},
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.13",
doi = "10.18653/v1/2023.emnlp-demo.13",
pages = "149--160",
abstract = "We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration. Our library allows researchers and practitioners to leverage adapter modularity through composition blocks, enabling the design of complex adapter setups. We demonstrate the library{'}s efficacy by evaluating its performance against full fine-tuning on various NLP tasks. Adapters provides a powerful tool for addressing the challenges of conventional fine-tuning paradigms and promoting more efficient and modular transfer learning. The library is available via https://adapterhub.ml/adapters.",
}
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%0 Conference Proceedings
%T Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
%A Poth, Clifton
%A Sterz, Hannah
%A Paul, Indraneil
%A Purkayastha, Sukannya
%A Engländer, Leon
%A Imhof, Timo
%A Vulić, Ivan
%A Ruder, Sebastian
%A Gurevych, Iryna
%A Pfeiffer, Jonas
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F poth-etal-2023-adapters
%X We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration. Our library allows researchers and practitioners to leverage adapter modularity through composition blocks, enabling the design of complex adapter setups. We demonstrate the library’s efficacy by evaluating its performance against full fine-tuning on various NLP tasks. Adapters provides a powerful tool for addressing the challenges of conventional fine-tuning paradigms and promoting more efficient and modular transfer learning. The library is available via https://adapterhub.ml/adapters.
%R 10.18653/v1/2023.emnlp-demo.13
%U https://aclanthology.org/2023.emnlp-demo.13
%U https://doi.org/10.18653/v1/2023.emnlp-demo.13
%P 149-160
Markdown (Informal)
[Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning](https://aclanthology.org/2023.emnlp-demo.13) (Poth et al., EMNLP 2023)
ACL
- Clifton Poth, Hannah Sterz, Indraneil Paul, Sukannya Purkayastha, Leon Engländer, Timo Imhof, Ivan Vulić, Sebastian Ruder, Iryna Gurevych, and Jonas Pfeiffer. 2023. Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 149–160, Singapore. Association for Computational Linguistics.