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        <title>Learned Adapters Are Better Than Manually Designed Adapters</title>
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    <name type="personal">
        <namePart type="given">Yuming</namePart>
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    <name type="personal">
        <namePart type="given">Peng</namePart>
        <namePart type="family">Wang</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Wei</namePart>
        <namePart type="family">Zhu</namePart>
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    <abstract>Recently, a series of works have looked into further improving the adapter-based tuning by manually designing better adapter architectures. Understandably, these manually designed solutions are sub-optimal. In this work, we propose the Learned Adapter framework to automatically learn the optimal adapter architectures for better task adaptation of pre-trained models (PTMs). First, we construct a unified search space for adapter architecture designs. In terms of the optimization method on the search space, we propose a simple-yet-effective method, GDNAS for better architecture optimization. Extensive experiments show that our Learned Adapter framework can outperform the previous parameter-efficient tuning (PETuning) baselines while tuning comparable or fewer parameters. Moreover: (a) the learned adapter architectures are explainable and transferable across tasks. (b) We demonstrate that our architecture search space design is valid.</abstract>
    <identifier type="citekey">zhang-etal-2023-learned</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-acl.468</identifier>
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        <url>https://aclanthology.org/2023.findings-acl.468</url>
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        <date>2023-07</date>
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%0 Conference Proceedings
%T Learned Adapters Are Better Than Manually Designed Adapters
%A Zhang, Yuming
%A Wang, Peng
%A Tan, Ming
%A Zhu, Wei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-learned
%X Recently, a series of works have looked into further improving the adapter-based tuning by manually designing better adapter architectures. Understandably, these manually designed solutions are sub-optimal. In this work, we propose the Learned Adapter framework to automatically learn the optimal adapter architectures for better task adaptation of pre-trained models (PTMs). First, we construct a unified search space for adapter architecture designs. In terms of the optimization method on the search space, we propose a simple-yet-effective method, GDNAS for better architecture optimization. Extensive experiments show that our Learned Adapter framework can outperform the previous parameter-efficient tuning (PETuning) baselines while tuning comparable or fewer parameters. Moreover: (a) the learned adapter architectures are explainable and transferable across tasks. (b) We demonstrate that our architecture search space design is valid.
%R 10.18653/v1/2023.findings-acl.468
%U https://aclanthology.org/2023.findings-acl.468
%U https://doi.org/10.18653/v1/2023.findings-acl.468
%P 7420-7437

Markdown (Informal)

[Learned Adapters Are Better Than Manually Designed Adapters](https://aclanthology.org/2023.findings-acl.468) (Zhang et al., Findings 2023)

• Learned Adapters Are Better Than Manually Designed Adapters (Zhang et al., Findings 2023)

ACL

• Yuming Zhang, Peng Wang, Ming Tan, and Wei Zhu. 2023. Learned Adapters Are Better Than Manually Designed Adapters. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7420–7437, Toronto, Canada. Association for Computational Linguistics.