Efficient Methods for Natural Language Processing: A Survey

Medientyp: E-Artikel
Titel: Efficient Methods for Natural Language Processing: A Survey
Beteiligte: Treviso, Marcos; Lee, Ji-Ung; Ji, Tianchu; Aken, Betty van; Cao, Qingqing; Ciosici, Manuel R.; Hassid, Michael; Heafield, Kenneth; Hooker, Sara; Raffel, Colin; Martins, Pedro H.; Martins, André F. T.; Forde, Jessica Zosa; Milder, Peter; Simpson, Edwin; Slonim, Noam; Dodge, Jesse; Strubell, Emma; Balasubramanian, Niranjan; Derczynski, Leon; Gurevych, Iryna; Schwartz, Roy
Erschienen: MIT Press, 2023
Erschienen in: Transactions of the Association for Computational Linguistics, 11 (2023), Seite 826-860
Sprache: Englisch
DOI: 10.1162/tacl_a_00577
ISSN: 2307-387X
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Beschreibung: AbstractRecent 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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