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University of Illinois at Urbana-Champaign

On the capabilities and risks of large language models

Abstract

dc:description

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.

Degree

thesis:*
Name thesis:degree_name
Ph.D.
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Computer Science
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Huang, Jie
Contributors dc:contributor
  • Chang, Kevin Chen-Chuan
  • Peng, Hao
  • Tong, Hanghang
  • Xu, Tianyin
  • Yang, Diyi

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • Copyright 2024 Jie Huang
Language dc:language
en, eng

Identifiers

dc:identifier.*
Handle dc:identifier
https://hdl.handle.net/2142/125556

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Huang, Jie. On the capabilities and risks of large language models. Dissertation thesis, University of Illinois at Urbana-Champaign, 2024. https://hdl.handle.net/2142/125556