Year: 2024
Type: dissertation
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.I am deeply grateful for the support and guidance I have received throughout my Ph.D. journey.First, I would like to express my sincere gratitude to my advisor, Kevin Chen-Chuan Chang.Together, we initiated and led the NLP research in our group.Kevin's support and guidance have been crucial in shaping
Author: Huang, Jie
Institution: University of Illinois Urbana-Champaign
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220806755