Date of Award
Summer 8-22-2024
Degree Type
Thesis
Degree Name
Master of Science (MS)
School
School of Computing
First Advisor
Noriko Tomuro, PhD
Second Advisor
Vahid Alizadeh, PhD
Third Advisor
Jamshid Sourati, PhD
Abstract
Large Language Models (LLMs) have shown exceptional performance in several natural language processing (NLP) tasks. Customizing LLMs boosts their performance in domain specific tasks but typically requires substantial resources and effort for training, such as supervised fine-tuning. This research proposes methods to achieve significant accuracy improvements given minimal resources, particularly focusing on open-ended question answering with a given piece of context. We utilize an LLM’s self-generated training data to fine-tune the LLM and partial fine-tuning with on-demand GPU to reduce practitioner training costs. The research shows that these methods give significant performance gains in a Retrieval Augmented Generation (RAG) based system on the Stanford Question Answering Dataset (SQuAD).
Recommended Citation
Lopez, Pablo, "Democratization of custom, high quality large language models" (2024). College of Computing and Digital Media Dissertations. 62.
https://via.library.depaul.edu/cdm_etd/62