Efficient Methods for Natural Language Processing: A Survey

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, Andre F.T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon DerczynskiIryna Gurevych, Roy Schwartz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve per¬formance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This mo¬tivates research into efficient methods that require fewer resources to achieve similar re¬sults. This survey synthesizes and relates cur¬rent methods and findings in efficient NLP. We aim to provide both guidance for con-ducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.

Original languageAmerican English
Pages (from-to)826-860
Number of pages35
JournalTransactions of the Association for Computational Linguistics
StatePublished - 2023

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© 2023 Association for Computational Linguistics.


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