Efficient Methods for Natural Language Processing: A Survey

被引：0

作者：

Treviso, Marcos ^{[1
]}

Lee, Ji-Ung ^{[2
]}

Ji, Tianchu ^{[3
]}

van Aken, Betty ^{[4
]}

Cao, Qingqing ^{[5
]}

Ciosici, Manuel R. ^{[6
]}

Hassid, Michael ^{[7
]}

Heafield, Kenneth ^{[8
]}

Hooker, Sara ^{[9
]}

Raffel, Colin ^{[10
]}

Martins, Pedro H. ^{[1
,11
]}

Martins, Andre F. T. ^{[1
,11
]}

Forde, Jessica Zosa ^{[12
]}

Milder, Peter ^{[16
]}

Simpson, Edwin ^{[13
]}

Slonim, Noam ^{[14
]}

Dodge, Jesse ^{[15
]}

Strubell, Emma ^{[15
,16
]}

Balasubramanian, Niranjan ^{[3
]}

Derczynski, Leon ^{[5
,17
]}

Gurevych, Iryna ^{[2
]}

Schwartz, Roy ^{[7
]}

机构：

[1] IST U Lisbon & Inst Telecomunicacoes, Lisbon, Portugal

[2] Tech Univ Darmstadt, Darmstadt, Germany

[3] SUNY Stony Brook, Stony Brook, NY USA

[4] Berliner Hsch Tech, Berlin, Germany

[5] Univ Washington, Washington, DC USA

[6] Univ Southern Calif, Los Angeles, CA USA

[7] Hebrew Univ Jerusalem, Jerusalem, Israel

[8] Univ Edinburgh, Edinburgh, Scotland

[9] Cohere AI, San Francisco, CA USA

[10] Univ North Carolina Chapel Hill, Chapel Hill, NC USA

[11] Unbabel, Lisbon, Portugal

[12] Brown Univ, Providence, RI USA

[13] Univ Bristol, Bristol, England

[14] IBM Res, Haifa, Israel

[15] Allen Inst AI, Seattle, WA USA

[16] Carnegie Mellon Univ, Pittsburgh, PA USA

[17] IT Univ Copenhagen, Copenhagen, Denmark

来源：

TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS | 2023年 / 11卷

基金：

欧洲研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：826 / 860

页数：35

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