자료유형  

 학위논문

Control Number  

0016934246

International Standard Book Number  

9798380615990

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Wang, Hong.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Santa Barbara., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(135 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

General Note  

Advisor: Yan, Xifeng.

Dissertation Note  

Thesis (Ph.D.)--University of California, Santa Barbara, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Natural language processing (NLP) has long been regarded as the pinnacle of artificial intelligence, aiming to achieve a comprehensive understanding of human languages. In recent years, the field has experienced significant advancements with the transition from rule-based approaches to deep learning methodologies. However, the standard approaches often rely on vast amounts of data for learning, highlighting the necessity for more data-efficient techniques. Additionally, effectively utilizing available resources while addressing the challenges of frequent model updates and safeguarding against malicious attacks that exploit limited resources presents another significant problem in NLP. This dissertation focuses on the development of efficient natural language processing (NLP) models under limited data and the effective utilization of available resources. In the first part, we address the challenge of learning models with limited data. For scenarios where only a few examples are available, we propose a meta-learning approach that leverages task-specific meta information to effectively learn new models. For cases with a moderate amount of data but still insufficient for more demanding tasks, we introduce self-supervised learning techniques to enhance performance by incorporating additional learning tasks from the available data. We also explore the limitations of even state-of-the-art language models, such as GPT-3, in handling out-of-distribution data shifts and propose a tutor-based learning approach that converts out-of-distribution problems into in-distribution ones through step-by-step demonstrations.In the second part, we shift our focus to optimizing resource utilization in NLP. Given the rapidly changing nature of the world, frequent updates of deployed models with new data are crucial. We present innovative approaches for effectively updating models in lifelong learning scenarios. As the adoption of large language models as backbone dialogue systems gains popularity, resource limitations become a significant concern. To counter malicious attacks, particularly Distributed Denial of Service (DDoS) attacks, we investigate the detection of bot imposters using a single question. By accurately distinguishing between human users and bots, our objective is to maximize resource allocation for real users and ensure uninterrupted service.

Subject Added Entry-Topical Term  

Computer science.

Index Term-Uncontrolled  

Natural language processing

Index Term-Uncontrolled  

Human languages

Index Term-Uncontrolled  

Data-efficient techniques

Index Term-Uncontrolled  

Meta-learning approach

Added Entry-Corporate Name  

University of California, Santa Barbara Computer Science

Host Item Entry  

Dissertations Abstracts International. 85-04B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641614