@article{treviso-etal-2023-efficient,
title = "Efficient Methods for Natural Language Processing: A Survey",
author = "Treviso, Marcos and
Lee, Ji-Ung and
Ji, Tianchu and
van Aken, Betty and
Cao, Qingqing and
Ciosici, Manuel R. and
Hassid, Michael and
Heafield, Kenneth and
Hooker, Sara and
Raffel, Colin and
Martins, Pedro H. and
Martins, Andr{\'e} F. T. and
Forde, Jessica Zosa and
Milder, Peter and
Simpson, Edwin and
Slonim, Noam and
Dodge, Jesse and
Strubell, Emma and
Balasubramanian, Niranjan and
Derczynski, Leon and
Gurevych, Iryna and
Schwartz, Roy",
journal = "Transactions of the Association for Computational Linguistics",
volume = "11",
year = "2023",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2023.tacl-1.48",
doi = "10.1162/tacl_a_00577",
pages = "826--860",
abstract = "Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.",
}
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<abstract>Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.</abstract>
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%0 Journal Article
%T Efficient Methods for Natural Language Processing: A Survey
%A Treviso, Marcos
%A Lee, Ji-Ung
%A Ji, Tianchu
%A van Aken, Betty
%A Cao, Qingqing
%A Ciosici, Manuel R.
%A Hassid, Michael
%A Heafield, Kenneth
%A Hooker, Sara
%A Raffel, Colin
%A Martins, Pedro H.
%A Martins, André F. T.
%A Forde, Jessica Zosa
%A Milder, Peter
%A Simpson, Edwin
%A Slonim, Noam
%A Dodge, Jesse
%A Strubell, Emma
%A Balasubramanian, Niranjan
%A Derczynski, Leon
%A Gurevych, Iryna
%A Schwartz, Roy
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F treviso-etal-2023-efficient
%X Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.
%R 10.1162/tacl_a_00577
%U https://aclanthology.org/2023.tacl-1.48
%U https://doi.org/10.1162/tacl_a_00577
%P 826-860
Markdown (Informal)
[Efficient Methods for Natural Language Processing: A Survey](https://aclanthology.org/2023.tacl-1.48) (Treviso et al., TACL 2023)
ACL
- Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, et al.. 2023. Efficient Methods for Natural Language Processing: A Survey. Transactions of the Association for Computational Linguistics, 11:826–860.