University of Illinois Urbana-Champaign

On the capabilities and risks of large language models

Huang, Jie

Content Files
HUANG-DISSERTATION-2024.pdf

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https://hdl.handle.net/2142/125556

Description

Title
On the capabilities and risks of large language models
Author(s)
Huang, Jie
Issue Date
2024-07-03
Director of Research (if dissertation) or Advisor (if thesis)
Chang, Kevin Chen-Chuan
Doctoral Committee Chair(s)
Chang, Kevin Chen-Chuan
Committee Member(s)
  • Peng, Hao
  • Tong, Hanghang
  • Xu, Tianyin
  • Yang, Diyi
Department of Study
Siebel Computing &DataScience
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • large language model
  • reasoning
  • privacy
  • ethics
Abstract
The advent of Large Language Models (LLMs) has significantly influenced the field of artificial intelligence, offering remarkable text generation capabilities through their vast number of parameters. These advancements have established new benchmarks across various domains. However, despite the impressive capabilities of LLMs, there exist critical limitations and ethical challenges. This dissertation critically examines the capabilities of LLMs, including their reasoning abilities, and explores potential risks, such as privacy leakage. Through this analysis, we underscore the crucial need to improve the capabilities of LLMs while mitigating the associated risks. Based on this understanding, we propose methodologies to augment and safeguard LLMs. To enhance their functionality, we develop techniques to integrate LLMs with external knowledge and design an innovative data structure for knowledge representation. Additionally, we advocate for incorporating citation mechanisms within LLMs to promote transparency, accountability, and respect for intellectual property. Through rigorous research and the introduction of cutting-edge techniques, this dissertation aims to advance the capabilities of LLMs while ensuring their responsible and ethical use, ultimately contributing to the development of powerful and trustworthy artificial intelligence systems.
Graduation Semester
2024-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/125556
Copyright and License Information
Copyright 2024 Jie Huang

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