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Efficient Natural Language Processing for Language Models
Efficient Natural Language Processing for Language Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211150951
- ISBN
- 9798382223490
- DDC
- 004
- 저자명
- Xu, Canwen.
- 서명/저자
- Efficient Natural Language Processing for Language Models
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 150 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- 주기사항
- Advisor: McAuley, Julian.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long inference delay prevent Transformer-based language models (LMs) from seeing broader adoption including for edge and mobile computing. Our efficient NLP research aims to comprehensively consider computation, time and carbon emission for the entire life-cycle of NLP, including data preparation, model training and inference.We demonstrate ways to promote computational efficiency in natural language processing, thus reducing hardware and software bottlenecks of training and inference, which is crucial in applying such models in production. Efficient NLP further facilitates democratization of language technology and allows language models to be accessible to more people.
- 일반주제명
- Computer science
- 일반주제명
- Statistics
- 일반주제명
- Information technology
- 키워드
- Language models
- 키워드
- Data efficiency
- 키워드
- Early exit
- 기타저자
- University of California, San Diego Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-10B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382223490
■035 ▼a(MiAaPQ)AAI30993169
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aXu, Canwen.
■24510▼aEfficient Natural Language Processing for Language Models
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a150 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: McAuley, Julian.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aDespite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long inference delay prevent Transformer-based language models (LMs) from seeing broader adoption including for edge and mobile computing. Our efficient NLP research aims to comprehensively consider computation, time and carbon emission for the entire life-cycle of NLP, including data preparation, model training and inference.We demonstrate ways to promote computational efficiency in natural language processing, thus reducing hardware and software bottlenecks of training and inference, which is crucial in applying such models in production. Efficient NLP further facilitates democratization of language technology and allows language models to be accessible to more people.
■590 ▼aSchool code: 0033.
■650 4▼aComputer science
■650 4▼aStatistics
■650 4▼aInformation technology
■653 ▼aNatural language processing
■653 ▼aLanguage models
■653 ▼aComputational efficiency
■653 ▼aData efficiency
■653 ▼aEarly exit
■690 ▼a0984
■690 ▼a0489
■690 ▼a0800
■690 ▼a0463
■71020▼aUniversity of California, San Diego▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160292▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
