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Constrained Inference and Decoding for Controlling Natural Language Processing Models.
Содержание
Constrained Inference and Decoding for Controlling Natural Language Processing Models.
자료유형  
 학위논문
Control Number  
0017162471
International Standard Book Number  
9798382836713
Dewey Decimal Classification Number  
004
Main Entry-Personal Name  
Meng, Tao.
Publication, Distribution, etc. (Imprint  
[S.l.] : University of California, Los Angeles., 2024
Publication, Distribution, etc. (Imprint  
Ann Arbor : ProQuest Dissertations & Theses, 2024
Physical Description  
137 p.
General Note  
Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
General Note  
Advisor: Chang, Kai-Wei.
Dissertation Note  
Thesis (Ph.D.)--University of California, Los Angeles, 2024.
Summary, Etc.  
요약With the rapid development of neural models in natural language processing (NLP), large and deep models achieve state-of-the-art across NLP tasks, and are deployed in real-world applications. Models become black-box to our human. Therefore, effective approaches controlling NLP models are demanding. Controlling helps model solve particular tasks. For example, when we ask the model to generate a recipe, we have a constraint about what ingredients we want the recipe to contain. In addition, as NLP researchers, we are responsible for preventing models from generating offensive or other unpredictable outputs, otherwise deploying them in real-world applications may cause society issues. To control the NLP models, my research focus on injecting constraints, a set of rules that the model must follow, to control the model behaviour via constrained inference and decoding. My research goal is to develop techniques leveraging different kinds of constraints in various scenarios for structure prediction models and large language models. Generally, constraints represent human knowledge and expectation to the model outputs, and constrained inference is the bridge between human beings and the neural models.
Subject Added Entry-Topical Term  
Computer science.
Subject Added Entry-Topical Term  
Information technology.
Index Term-Uncontrolled  
Constrained decoding
Index Term-Uncontrolled  
Constrained inference
Index Term-Uncontrolled  
Constraints
Index Term-Uncontrolled  
Large language models
Index Term-Uncontrolled  
Natural language processing
Added Entry-Corporate Name  
University of California, Los Angeles Computer Science 0201
Host Item Entry  
Dissertations Abstracts International. 85-12B.
Electronic Location and Access  
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Control Number  
joongbu:654670
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