@inproceedings{chen-etal-2022-inducer,
title = "Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning",
author = "Chen, Yifan and
Hazarika, Devamanyu and
Namazifar, Mahdi and
Liu, Yang and
Jin, Di and
Hakkani-Tur, Dilek",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.50/",
doi = "10.18653/v1/2022.emnlp-main.50",
pages = "793--808",
abstract = "Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM), prefix-tuning can obtain strong performance by training only a small portion of parameters. In this paper, we propose to understand and further develop prefix-tuning through the kernel lens. Specifically, we make an analogy between \textit{prefixes} and \textit{inducing variables} in kernel methods and hypothesize that \textit{prefixes} serving as \textit{inducing variables} would improve their overall mechanism. From the kernel estimator perspective, we suggest a new variant of prefix-tuning{---}\textit{inducer-tuning}, which shares the exact mechanism as prefix-tuning while leveraging the residual form found in adapter-tuning. This mitigates the initialization issue in prefix-tuning. Through comprehensive empirical experiments on natural language understanding and generation tasks, we demonstrate that inducer-tuning can close the performance gap between prefix-tuning and fine-tuning."
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<abstract>Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM), prefix-tuning can obtain strong performance by training only a small portion of parameters. In this paper, we propose to understand and further develop prefix-tuning through the kernel lens. Specifically, we make an analogy between prefixes and inducing variables in kernel methods and hypothesize that prefixes serving as inducing variables would improve their overall mechanism. From the kernel estimator perspective, we suggest a new variant of prefix-tuning—inducer-tuning, which shares the exact mechanism as prefix-tuning while leveraging the residual form found in adapter-tuning. This mitigates the initialization issue in prefix-tuning. Through comprehensive empirical experiments on natural language understanding and generation tasks, we demonstrate that inducer-tuning can close the performance gap between prefix-tuning and fine-tuning.</abstract>
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%0 Conference Proceedings
%T Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning
%A Chen, Yifan
%A Hazarika, Devamanyu
%A Namazifar, Mahdi
%A Liu, Yang
%A Jin, Di
%A Hakkani-Tur, Dilek
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-etal-2022-inducer
%X Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM), prefix-tuning can obtain strong performance by training only a small portion of parameters. In this paper, we propose to understand and further develop prefix-tuning through the kernel lens. Specifically, we make an analogy between prefixes and inducing variables in kernel methods and hypothesize that prefixes serving as inducing variables would improve their overall mechanism. From the kernel estimator perspective, we suggest a new variant of prefix-tuning—inducer-tuning, which shares the exact mechanism as prefix-tuning while leveraging the residual form found in adapter-tuning. This mitigates the initialization issue in prefix-tuning. Through comprehensive empirical experiments on natural language understanding and generation tasks, we demonstrate that inducer-tuning can close the performance gap between prefix-tuning and fine-tuning.
%R 10.18653/v1/2022.emnlp-main.50
%U https://aclanthology.org/2022.emnlp-main.50/
%U https://doi.org/10.18653/v1/2022.emnlp-main.50
%P 793-808
Markdown (Informal)
[Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning](https://aclanthology.org/2022.emnlp-main.50/) (Chen et al., EMNLP 2022)
ACL
- Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, and Dilek Hakkani-Tur. 2022. Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 793–808, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.