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Constrained Inference and Decoding f...

Meng, Tao.

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Constrained Inference and Decoding for Controlling Natural Language Processing Models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Constrained Inference and Decoding for Controlling Natural Language Processing Models./
作者:	Meng, Tao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	137 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31331729
ISBN:	9798382836713

Constrained Inference and Decoding for Controlling Natural Language Processing Models.
Meng, Tao.

Constrained Inference and Decoding for Controlling Natural Language Processing Models. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 137 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

With 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.

ISBN: 9798382836713Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Constrained decoding

Constrained Inference and Decoding for Controlling Natural Language Processing Models.
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500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Chang, Kai-Wei.
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520 $a With 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.
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653 $a Constrained inference
653 $a Constraints
653 $a Large language models
653 $a Natural language processing
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690 $a 0984
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