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Constrained Inference and Decoding for Controlling Natural Language Processing Models.
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
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:654670